From 63133aa1bda1ef101772b1d85e27b8d320d32412 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Tue, 12 Feb 2019 22:34:19 +0100 Subject: re-running reports.. --- scraper/reports/paper_title_report.html | 4 ++-- scraper/reports/paper_title_report_no_location.html | 4 ++-- scraper/reports/paper_title_report_nonmatching.html | 4 ++-- scraper/s2-final-report.py | 2 ++ scraper/s2-geocode-server.py | 4 ++++ scraper/s2-papers.py | 4 ++++ scraper/util.py | 4 ++-- 7 files changed, 18 insertions(+), 8 deletions(-) (limited to 'scraper') diff --git a/scraper/reports/paper_title_report.html b/scraper/reports/paper_title_report.html index adfec23b..90deaf36 100644 --- a/scraper/reports/paper_title_report.html +++ b/scraper/reports/paper_title_report.html @@ -1,3 +1,3 @@ -Paper Title Sanity Check

Paper Title Sanity Check

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
a_pascal_yahooaPascalDescribing Objects by their AttributesDescribing objects by their attributes[pdf][s2]2e384f057211426ac5922f1b33d2aa8df5d51f57
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from MoviesCollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf][s2]Australian National Universityb1f4423c227fa37b9680787be38857069247a307
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]University of California, Irvine0e986f51fe45b00633de9fd0c94d082d2be51406
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
berkeley_poseBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
bjut_3dBJUT-3DThe BJUT-3D Large-Scale Chinese Face DatabaseA novel face recognition method based on 3D face model[pdf][s2]1ed1a49534ad8dd00f81939449f6389cfbc25321
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
brainwashBrainwashBrainwash datasetBrainwash: A Data System for Feature Engineering[pdf][s2]214c966d1f9c2a4b66f4535d9a0d4078e63a5867
bu_3dfeBU-3DFEA 3D Facial Expression Database For Facial Behavior ResearchA 3D facial expression database for facial behavior research[pdf][s2]SUNY Binghamtoncc589c499dcf323fe4a143bbef0074c3e31f9b60
buhmap_dbBUHMAP-DBFacial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
cafeCAFEThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the ArtPedestrian Detection: An Evaluation of the State of the Art[pdf][s2]f72f6a45ee240cc99296a287ff725aaa7e7ebb35
camelCAMELCAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and TrackingApplication of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis[pdf][s2]5801690199c1917fa58c35c3dead177c0b8f9f2d
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
celebaCelebADeep Learning Face Attributes in the WildDeep Learning Face Attributes in the Wild[pdf][s2]Chinese University of Hong Kong6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4
celeba_plusCelebFaces+Deep Learning Face Representation from Predicting 10,000 ClassesDeep Learning Face Representation from Predicting 10,000 Classes[pdf][s2]Shenzhen Institutes of Advanced Technology177bc509dd0c7b8d388bb47403f28d6228c14b5c
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
chokepointChokePointPatch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face RecognitionPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf][s2]University of Queensland0486214fb58ee9a04edfe7d6a74c6d0f661a7668
cityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene UnderstandingThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf][s2]32cde90437ab5a70cf003ea36f66f2de0e24b3ab
cityscapesCityscapesThe Cityscapes DatasetThe Cityscapes Dataset[pdf][s2]5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
cmdpCMDPDistance Estimation of an Unknown Person from a PortraitDistance Estimation of an Unknown Person from a Portrait[pdf][s2]California Institute of Technology56ae6d94fc6097ec4ca861f0daa87941d1c10b70
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
cohn_kanadeCKComprehensive Database for Facial Expression AnalysisComprehensive Database for Facial Expression Analysis[pdf][s2]Carnegie Mellon University23fc83c8cfff14a16df7ca497661264fc54ed746
cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expressionThe Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf][s2]4d9a02d080636e9666c4d1cc438b9893391ec6c7
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]06f02199690961ba52997cde1527e714d2b3bf8f
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]Jacobs University070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
deep_fashionDeepFashionFashion Landmark Detection in the WildFashion Landmark Detection in the Wild[pdf][s2]Chinese University of Hong Kong4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7
disfaDISFADISFA: A Spontaneous Facial Action Intensity DatabaseExtended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions[pdf][s2]a5acda0e8c0937bfed013e6382da127103e41395
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene AnalysisDepth and Appearance for Mobile Scene Analysis[pdf][s2]13f06b08f371ba8b5d31c3e288b4deb61335b462
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
expwExpWLearning Social Relation Traits from Face ImagesLearning Social Relation Traits from Face Images[pdf][s2]Chinese University of Hong Kong2a171f8d14b6b8735001a11c217af9587d095848
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
face_research_labFace Research Lab LondonFace Research Lab London Set. figshareAnxiety promotes memory for mood-congruent faces but does not alter loss aversion.[pdf][s2]University College Londonc6526dd3060d63a6c90e8b7ff340383c4e0e0dd8
face_scrubFaceScrubA data-driven approach to cleaning large face datasetsA data-driven approach to cleaning large face datasets[pdf][s2]University of Illinois, Urbana-Champaign0d3bb75852098b25d90f31d2f48fd0cb4944702b
face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with FacesFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf][s2]Columbia University4c170a0dcc8de75587dae21ca508dab2f9343974
face_tracerFaceTracerFace Swapping: Automatically Replacing Faces in PhotographsFace swapping: automatically replacing faces in photographs[pdf][s2]Columbia University670637d0303a863c1548d5b19f705860a23e285c
facebook_100Facebook100Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
families_in_the_wildFIWVisual Kinship Recognition of Families in the WildVisual Kinship Recognition of Families in the Wild[pdf][s2]dd65f71dac86e36eecbd3ed225d016c3336b4a13
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]University of Massachusetts75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroGeneralização cartográfica automatizada para um banco de dados cadastral[pdf][s2]b6b1b0632eb9d4ab1427278f5e5c46f97753c73d
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
fiw_300300-W300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf][s2]University of Twente044d9a8c61383312cdafbcc44b9d00d650b21c70
fiw_300300-WA semi-automatic methodology for facial landmark annotationA Semi-automatic Methodology for Facial Landmark Annotation[pdf][s2]University of Twente013909077ad843eb6df7a3e8e290cfd5575999d2
frav3dFRAV3DMULTIMODAL 2D, 2.5D & 3D FACE VERIFICATIONMultimodal 2D, 2.5D & 3D Face Verification[pdf][s2]2b926b3586399d028b46315d7d9fb9d879e4f79c
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
gallagherGallagherClothing Cosegmentation for Recognizing PeopleClothing cosegmentation for recognizing people[pdf][s2]22ad2c8c0f4d6aa4328b38d894b814ec22579761
gavab_dbGavabGavabDB: a 3D face databaseExpression invariant 3D face recognition with a Morphable Model[pdf][s2]42505464808dfb446f521fc6ff2cfeffd4d68ff1
geofacesGeoFacesGeoFaceExplorer: Exploring the Geo-Dependence of Facial AttributesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf][s2]University of Kentucky17b46e2dad927836c689d6787ddb3387c6159ece
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
grazGraz PedestrianGeneric Object Recognition with BoostingGeneric object recognition with boosting[pdf][s2]2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
grazGraz PedestrianObject Recognition Using Segmentation for Feature DetectionObject recognition using segmentation for feature detection[pdf][s2]12ad3b5bbbf407f8e54ea692c07633d1a867c566
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
helenHelenInteractive Facial Feature LocalizationInteractive Facial Feature Localization[pdf][s2]University of Illinois, Urbana-Champaign95f12d27c3b4914e0668a268360948bce92f7db3
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHigh-resolution comprehensive 3-D dynamic database for facial articulation analysis[pdf][s2]24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd
hipsterwarsHipsterwarsHipster Wars: Discovering Elements of Fashion StylesHipster Wars: Discovering Elements of Fashion Styles[pdf][s2]Tohoku University04c2cda00e5536f4b1508cbd80041e9552880e67
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hrt_transgenderHRT TransgenderIs the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender DatasetIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf][s2]University of North Carolina Wilmington137aa2f891d474fce1e7a1d1e9b3aefe21e22b34
hrt_transgenderHRT TransgenderInvestigating the Periocular-Based Face Recognition Across Gender TransformationInvestigating the Periocular-Based Face Recognition Across Gender Transformation[pdf][s2]University of North Carolina at Wilmington2f43b614607163abf41dfe5d17ef6749a1b61304
hrt_transgenderHRT TransgenderFace recognition across gender transformation using SVM ClassifierFace recognition: A literature survey[pdf][s2]28312c3a47c1be3a67365700744d3d6665b86f22
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ifdbIFDBIranian Face Database with age, pose and expressionIranian Face Database with age, pose and expression[pdf][s2]b71d1aa90dcbe3638888725314c0d56640c1fef1
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
ijb_cIJB-APushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
ijb_cIJB-BIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]21d9d0deed16f0ad62a4865e9acf0686f4f15492
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
imfdbIMFDBIndian Movie Face Database: A Benchmark for Face Recognition Under Wide VariationsIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf][s2]ca3e88d87e1344d076c964ea89d91a75c417f5ee
imm_faceIMM Face DatasetThe IMM Face Database - An Annotated Dataset of 240 Face ImagesAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image UnderstandingSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf][s2]University of Washington51eba481dac6b229a7490f650dff7b17ce05df73
inria_personINRIA PedestrianHistograms of Oriented Gradients for Human DetectionHistograms of oriented gradients for human detection[pdf][s2]10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5
jaffeJAFFECoding Facial Expressions with Gabor WaveletsCoding Facial Expressions with Gabor Wavelets[pdf][s2]Kyushu University45c31cde87258414f33412b3b12fc5bec7cb3ba9
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
kdefKDEFThe Karolinska Directed Emotional Faces – KDEFGaze fixation and the neural circuitry of face processing in autism[pdf][s2]93884e46c49f7ae1c7c34046fbc28882f2bd6341
kin_faceUB KinFaceGenealogical Face Recognition based on UB KinFace DatabaseGenealogical face recognition based on UB KinFace database[pdf][s2]SUNY Buffalo2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship Verification through Transfer Learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
kinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face RecognitionKinectFaceDB: A Kinect Database for Face Recognition[pdf][s2]0b440695c822a8e35184fb2f60dcdaa8a6de84ae
kittiKITTIVision meets Robotics: The KITTI DatasetThe Role of Machine Vision for Intelligent Vehicles[pdf][s2]35ba4ebfd017a56b51e967105af9ae273c9b0178
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning Effective Human Pose Estimation from Inaccurate AnnotationLearning effective human pose estimation from inaccurate annotation[pdf][s2]4e4746094bf60ee83e40d8597a6191e463b57f76
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]University of Massachusetts2d3482dcff69c7417c7b933f22de606a0e8e42d4
lfw_aLFW-aEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
lfw_pLFWPLocalizing Parts of Faces Using a Consensus of ExemplarsLocalizing Parts of Faces Using a Consensus of Exemplars[pdf][s2]Columbia University140438a77a771a8fb656b39a78ff488066eb6b50
m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
maflMAFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]Chinese University of Hong Kong8a3c5507237957d013a0fe0f082cab7f757af6ee
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]Chinese Academy of Sciences45e616093a92e5f1e61a7c6037d5f637aa8964af
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosRobust semi-automatic head pose labeling for real-world face video sequences[pdf][s2]McGill Universityc570d1247e337f91e555c3be0e8c8a5aba539d9f
mcgillMcGill Real WorldRobust Semi-automatic Head Pose Labeling for Real-World Face Video SequencesRobust semi-automatic head pose labeling for real-world face video sequences[pdf][s2]McGill Universityc570d1247e337f91e555c3be0e8c8a5aba539d9f
megaageMegaAgeQuantifying Facial Age by Posterior of Age ComparisonsQuantifying Facial Age by Posterior of Age Comparisons[pdf][s2]Chinese University of Hong Kongd80a3d1f3a438e02a6685e66ee908446766fefa9
megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at ScaleThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf][s2]University of Washington96e0cfcd81cdeb8282e29ef9ec9962b125f379b0
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
mifsMIFSSpoofing Faces Using Makeup: An Investigative StudySpoofing faces using makeup: An investigative study[pdf][s2]INRIA Méditerranée23e824d1dfc33f3780dd18076284f07bd99f1c43
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
miwMIWAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
motMOTLearning to associate: HybridBoosted multi-target tracker for crowded sceneLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpii_gazeMPIIGazeAppearance-based Gaze Estimation in the WildAppearance-based gaze estimation in the wild[pdf][s2]Max Planck Institute for Informatics0df0d1adea39a5bef318b74faa37de7f3e00b452
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
muctMUCTThe MUCT Landmarked Face DatabaseAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
mug_facesMUG FacesThe MUG Facial Expression DatabaseThe MUG facial expression database[pdf][s2]Aristotle University of Thessalonikif1af714b92372c8e606485a3982eab2f16772ad8
multi_pieMULTIPIEMulti-PIEScheduling heterogeneous multi-cores through performance impact estimation (PIE)[pdf][s2]109df0e8e5969ddf01e073143e83599228a1163f
names_and_faces_newsNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nd_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face RecognitionUsing a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf][s2]fd8168f1c50de85bac58a8d328df0a50248b16ae
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]7f4040b482d16354d5938c1d1b926b544652bf5b
nudedetectionNude DetectionA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classi cationSummary of Research on Informant Accuracy in Network Data, 11 and on the Reverse Small World Problem[pdf][s2]fb82681ac5d3487bd8e52dbb3d1fa220eeac855e
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pornodbPornography DBPooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
prwPRWPerson Re-identification in the WildPerson Re-identification in the Wild[pdf][s2]University of Technology Sydney0b84f07af44f964817675ad961def8a51406dd2e
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
pubfigPubFigAttribute and Simile Classifiers for Face VerificationAttribute and simile classifiers for face verification[pdf][s2]Columbia University759a3b3821d9f0e08e0b0a62c8b693230afc3f8d
pubfig_83pubfig83Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
put_facePut FaceThe PUT face databaseLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
qmul_gridGRIDMulti-Camera Activity Correlation AnalysisMulti-camera activity correlation analysis[pdf][s2]3b5b6d19d4733ab606c39c69a889f9e67967f151
qmul_gridGRIDTime-delayed correlation analysis for multi-camera activity understandingTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf][s2]2edb87494278ad11641b6cf7a3f8996de12b8e14
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
scfaceSCfaceSCface – surveillance cameras face databaseLarge Variability Surveillance Camera Face Database[pdf][s2]f3b84a03985de3890b400b68e2a92c0a00afd9d0
scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty PerceptionSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf][s2]South China University of Technologybd26dabab576adb6af30484183c9c9c8379bf2e0
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
sheffieldSheffield FaceFace Recognition: From Theory to ApplicationsFace Description with Local Binary Patterns: Application to Face Recognition[pdf][s2]3607afdb204de9a5a9300ae98aa4635d9effcda2
social_relationSocial RelationFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
social_relationSocial RelationLearning Social Relation Traits from Face ImagesLearning Social Relation Traits from Face Images[pdf][s2]Chinese University of Hong Kong2a171f8d14b6b8735001a11c217af9587d095848
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A Video Dataset for Sports AnalysisSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf][s2]Michigan State University1a40092b493c6b8840257ab7f96051d1a4dbfeb2
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
sun_attributesSUNSUN Attribute Database: +Paper Title Sanity Check

Paper Title Sanity Check

keynameour titlefound titleaddresss2 id
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
fiw_300300-WA semi-automatic methodology for facial landmark annotationA Semi-automatic Methodology for Facial Landmark Annotation[pdf][s2]University of Twente013909077ad843eb6df7a3e8e290cfd5575999d2
buhmap_dbBUHMAP-DBFacial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
kittiKITTIVision meets Robotics: The KITTI DatasetVision meets robotics: The KITTI dataset[pdf][s2]026e3363b7f76b51cc711886597a44d5f1fd1de2
ilids_mctsi-LIDSImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
fiw_300300-W300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge[pdf][s2]University of Twente044d9a8c61383312cdafbcc44b9d00d650b21c70
chokepointChokePointPatch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face RecognitionPatch-based probabilistic image quality assessment for face selection and improved video-based face recognition[pdf][s2]University of Queensland0486214fb58ee9a04edfe7d6a74c6d0f661a7668
hipsterwarsHipsterwarsHipster Wars: Discovering Elements of Fashion StylesHipster Wars: Discovering Elements of Fashion Styles[pdf][s2]Tohoku University04c2cda00e5536f4b1508cbd80041e9552880e67
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]06f02199690961ba52997cde1527e714d2b3bf8f
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]Jacobs University070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
uccsUCCSLarge scale unconstrained open set face databaseLarge scale unconstrained open set face database[pdf][s2]University of Colorado at Colorado Springs07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
kinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face RecognitionKinectFaceDB: A Kinect Database for Face Recognition[pdf][s2]0b440695c822a8e35184fb2f60dcdaa8a6de84ae
prwPRWPerson Re-identification in the WildPerson Re-identification in the Wild[pdf][s2]University of Technology Sydney0b84f07af44f964817675ad961def8a51406dd2e
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
ijb_cIJB-BIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
face_scrubFaceScrubA data-driven approach to cleaning large face datasetsA data-driven approach to cleaning large face datasets[pdf][s2]University of Illinois, Urbana-Champaign0d3bb75852098b25d90f31d2f48fd0cb4944702b
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
mpii_gazeMPIIGazeAppearance-based Gaze Estimation in the WildAppearance-based gaze estimation in the wild[pdf][s2]Max Planck Institute for Informatics0df0d1adea39a5bef318b74faa37de7f3e00b452
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]University of California, Irvine0e986f51fe45b00633de9fd0c94d082d2be51406
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
inria_personINRIA PedestrianHistograms of Oriented Gradients for Human DetectionHistograms of oriented gradients for human detection[pdf][s2]10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5
grazGraz PedestrianObject Recognition Using Segmentation for Feature DetectionObject recognition using segmentation for feature detection[pdf][s2]12ad3b5bbbf407f8e54ea692c07633d1a867c566
lfw_aLFW-aEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
hrt_transgenderHRT TransgenderIs the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender DatasetIs the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset[pdf][s2]University of North Carolina Wilmington137aa2f891d474fce1e7a1d1e9b3aefe21e22b34
eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene AnalysisDepth and Appearance for Mobile Scene Analysis[pdf][s2]13f06b08f371ba8b5d31c3e288b4deb61335b462
lfw_pLFWPLocalizing Parts of Faces Using a Consensus of ExemplarsLocalizing Parts of Faces Using a Consensus of Exemplars[pdf][s2]Columbia University140438a77a771a8fb656b39a78ff488066eb6b50
ijb_cIJB-APushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
celeba_plusCelebFaces+Deep Learning Face Representation from Predicting 10,000 ClassesDeep Learning Face Representation from Predicting 10,000 Classes[pdf][s2]Shenzhen Institutes of Advanced Technology177bc509dd0c7b8d388bb47403f28d6228c14b5c
geofacesGeoFacesGeoFaceExplorer: Exploring the Geo-Dependence of Facial AttributesGeoFaceExplorer: exploring the geo-dependence of facial attributes[pdf][s2]University of Kentucky17b46e2dad927836c689d6787ddb3387c6159ece
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf][s2]18858cc936947fc96b5c06bbe3c6c2faa5614540
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
sports_videos_in_the_wildSVWSports Videos in the Wild (SVW): A Video Dataset for Sports AnalysisSports Videos in the Wild (SVW): A video dataset for sports analysis[pdf][s2]Michigan State University1a40092b493c6b8840257ab7f96051d1a4dbfeb2
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
cafeCAFEThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]21d9d0deed16f0ad62a4865e9acf0686f4f15492
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf][s2]2258e01865367018ed6f4262c880df85b94959f8
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
gallagherGallagherClothing Cosegmentation for Recognizing PeopleClothing cosegmentation for recognizing people[pdf][s2]22ad2c8c0f4d6aa4328b38d894b814ec22579761
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
mifsMIFSSpoofing Faces Using Makeup: An Investigative StudySpoofing faces using makeup: An investigative study[pdf][s2]INRIA Méditerranée23e824d1dfc33f3780dd18076284f07bd99f1c43
cohn_kanadeCKComprehensive Database for Facial Expression AnalysisComprehensive Database for Facial Expression Analysis[pdf][s2]Carnegie Mellon University23fc83c8cfff14a16df7ca497661264fc54ed746
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
scfaceSCfaceSCface – surveillance cameras face databaseSCface – surveillance cameras face database[pdf][s2]29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d
social_relationSocial RelationLearning Social Relation Traits from Face ImagesLearning Social Relation Traits from Face Images[pdf][s2]Chinese University of Hong Kong2a171f8d14b6b8735001a11c217af9587d095848
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
frav3dFRAV3DMULTIMODAL 2D, 2.5D & 3D FACE VERIFICATIONMultimodal 2D, 2.5D & 3D Face Verification[pdf][s2]2b926b3586399d028b46315d7d9fb9d879e4f79c
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]University of Massachusetts2d3482dcff69c7417c7b933f22de606a0e8e42d4
a_pascal_yahooaPascalDescribing Objects by their AttributesDescribing objects by their attributes[pdf][s2]2e384f057211426ac5922f1b33d2aa8df5d51f57
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
kin_faceUB KinFaceGenealogical Face Recognition based on UB KinFace DatabaseGenealogical face recognition based on UB KinFace database[pdf][s2]SUNY Buffalo2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d
qmul_gridGRIDTime-delayed correlation analysis for multi-camera activity understandingTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf][s2]2edb87494278ad11641b6cf7a3f8996de12b8e14
grazGraz PedestrianGeneric Object Recognition with BoostingGeneric object recognition with boosting[pdf][s2]2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9
hrt_transgenderHRT TransgenderInvestigating the Periocular-Based Face Recognition Across Gender TransformationInvestigating the Periocular-Based Face Recognition Across Gender Transformation[pdf][s2]University of North Carolina at Wilmington2f43b614607163abf41dfe5d17ef6749a1b61304
names_and_faces_newsNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
cityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene UnderstandingThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf][s2]32cde90437ab5a70cf003ea36f66f2de0e24b3ab
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
widerWIDERRecognize Complex Events from Static Images by Fusing Deep ChannelsRecognize complex events from static images by fusing deep channels[pdf][s2]Shenzhen Institutes of Advanced Technology356b431d4f7a2a0a38cf971c84568207dcdbf189
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline ResultsPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf][s2]Johns Hopkins University377f2b65e6a9300448bdccf678cde59449ecd337
vmuVMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
youtube_makeupYMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
qmul_gridGRIDMulti-Camera Activity Correlation AnalysisMulti-camera activity correlation analysis[pdf][s2]3b5b6d19d4733ab606c39c69a889f9e67967f151
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
tud_multiviewTUD-MultiviewMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tud_stadtmitteTUD-StadtmitteMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
wider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical ContextsHuman Attribute Recognition by Deep Hierarchical Contexts[pdf][s2]Chinese University of Hong Kong44d23df380af207f5ac5b41459c722c87283e1eb
vadanaVADANAVADANA: A dense dataset for facial image analysisVADANA: A dense dataset for facial image analysis[pdf][s2]4563b46d42079242f06567b3f2e2f7a80cb3befe
jaffeJAFFECoding Facial Expressions with Gabor WaveletsCoding Facial Expressions with Gabor Wavelets[pdf][s2]Kyushu University45c31cde87258414f33412b3b12fc5bec7cb3ba9
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]Chinese Academy of Sciences45e616093a92e5f1e61a7c6037d5f637aa8964af
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship Verification through Transfer Learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
face_tracerFaceTracerFaceTracer: A Search Engine for Large Collections of Images with FacesFaceTracer: A Search Engine for Large Collections of Images with Faces[pdf][s2]Columbia University4c170a0dcc8de75587dae21ca508dab2f9343974
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expressionThe Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf][s2]4d9a02d080636e9666c4d1cc438b9893391ec6c7
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning Effective Human Pose Estimation from Inaccurate AnnotationLearning effective human pose estimation from inaccurate annotation[pdf][s2]4e4746094bf60ee83e40d8597a6191e463b57f76
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
deep_fashionDeepFashionFashion Landmark Detection in the WildFashion Landmark Detection in the Wild[pdf][s2]Chinese University of Hong Kong4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7
violent_flowsViolent FlowsViolent Flows: Real-Time Detection of Violent Crowd BehaviorViolent flows: Real-time detection of violent crowd behavior[pdf][s2]5194cbd51f9769ab25260446b4fa17204752e799
imsituimSituSituation Recognition: Visual Semantic Role Labeling for Image UnderstandingSituation Recognition: Visual Semantic Role Labeling for Image Understanding[pdf][s2]University of Washington51eba481dac6b229a7490f650dff7b17ce05df73
wider_faceWIDER FACEWIDER FACE: A Face Detection BenchmarkWIDER FACE: A Face Detection Benchmark[pdf][s2]Chinese University of Hong Kong52d7eb0fbc3522434c13cc247549f74bb9609c5d
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
youtube_facesYouTubeFacesFace Recognition in Unconstrained Videos with Matched Background SimilarityFace recognition in unconstrained videos with matched background similarity[pdf][s2]Open University of Israel560e0e58d0059259ddf86fcec1fa7975dee6a868
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
cmdpCMDPDistance Estimation of an Unknown Person from a PortraitDistance Estimation of an Unknown Person from a Portrait[pdf][s2]California Institute of Technology56ae6d94fc6097ec4ca861f0daa87941d1c10b70
unbc_shoulder_painUNBC-McMaster PainPAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive DatabasePainful data: The UNBC-McMaster shoulder pain expression archive database[pdf][s2]56ffa7d906b08d02d6d5a12c7377a57e24ef3391
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
motMOTLearning to associate: HybridBoosted multi-target tracker for crowded sceneLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
disfaDISFADISFA: A Spontaneous Facial Action Intensity DatabaseDISFA: A Spontaneous Facial Action Intensity Database[pdf][s2]5a5f0287484f0d480fed1ce585dbf729586f0edc
wlfdbWLFDBWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
cityscapesCityscapesThe Cityscapes DatasetThe Cityscapes Dataset[pdf][s2]5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
celebaCelebADeep Learning Face Attributes in the WildDeep Learning Face Attributes in the Wild[pdf][s2]Chinese University of Hong Kong6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
face_tracerFaceTracerFace Swapping: Automatically Replacing Faces in PhotographsFace swapping: automatically replacing faces in photographs[pdf][s2]Columbia University670637d0303a863c1548d5b19f705860a23e285c
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
pubfigPubFigAttribute and Simile Classifiers for Face VerificationAttribute and simile classifiers for face verification[pdf][s2]Columbia University759a3b3821d9f0e08e0b0a62c8b693230afc3f8d
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]University of Massachusetts75da1df4ed319926c544eefe17ec8d720feef8c0
urban_tribesUrban TribesFrom Bikers to Surfers: Visual Recognition of Urban TribesFrom Bikers to Surfers: Visual Recognition of Urban Tribes[pdf][s2]Columbia University774cbb45968607a027ae4729077734db000a1ec5
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
berkeley_poseBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
nudedetectionNude DetectionA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
vgg_celebs_in_placesCIPFaces in Places: Compound Query RetrievalFaces in Places: compound query retrieval[pdf][s2]University of Oxford7ebb153704706e457ab57b432793d2b6e5d12592
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]7f4040b482d16354d5938c1d1b926b544652bf5b
sun_attributesSUNSUN Attribute Database: Discovering, Annotating, and Recognizing Scene AttributesSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf][s2]833fa04463d90aab4a9fe2870d480f0b40df446e
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_multiviewTUD-MultiviewMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_stadtmitteTUD-StadtmitteMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
uccsUCCSLarge scale unconstrained open set face databaseLarge scale unconstrained open set face database[pdf][s2]University of Colorado at Colorado Springs07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1
ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildUCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf][s2]University of Central Floridab5f2846a506fc417e7da43f6a7679146d99c5e96
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
ufddUFDDPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline ResultsPushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results[pdf][s2]Johns Hopkins University377f2b65e6a9300448bdccf678cde59449ecd337
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
unbc_shoulder_painUNBC-McMaster PainPAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive DatabasePainful data: The UNBC-McMaster shoulder pain expression archive database[pdf][s2]56ffa7d906b08d02d6d5a12c7377a57e24ef3391
urban_tribesUrban TribesFrom Bikers to Surfers: Visual Recognition of Urban TribesFrom Bikers to Surfers: Visual Recognition of Urban Tribes[pdf][s2]Columbia University774cbb45968607a027ae4729077734db000a1ec5
usedUSED Social Event DatasetUSED: A Large-scale Social Event Detection DatasetUSED: a large-scale social event detection dataset[pdf][s2]8627f019882b024aef92e4eb9355c499c733e5b7
v47V47Re-identification of Pedestrians with Variable Occlusion and ScaleRe-identification of pedestrians with variable occlusion and scale[pdf][s2]922e0a51a3b8c67c4c6ac09a577ff674cbd28b34
vadanaVADANAVADANA: A dense dataset for facial image analysisVADANA: A dense dataset for facial image analysis[pdf][s2]4563b46d42079242f06567b3f2e2f7a80cb3befe
vgg_celebs_in_placesCIPFaces in Places: Compound Query RetrievalFaces in Places: compound query retrieval[pdf][s2]University of Oxford7ebb153704706e457ab57b432793d2b6e5d12592
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
vgg_faces2VGG Face2VGGFace2: A dataset for recognising faces across pose and ageVGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf][s2]University of Oxfordeb027969f9310e0ae941e2adee2d42cdf07d938c
violent_flowsViolent FlowsViolent Flows: Real-Time Detection of Violent Crowd BehaviorViolent flows: Real-time detection of violent crowd behavior[pdf][s2]5194cbd51f9769ab25260446b4fa17204752e799
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
visual_phrasesPhrasal RecognitionRecognition using Visual PhrasesRecognition using visual phrases[pdf][s2]e8de844fefd54541b71c9823416daa238be65546
vmuVMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
widerWIDERRecognize Complex Events from Static Images by Fusing Deep ChannelsRecognize complex events from static images by fusing deep channels[pdf][s2]Shenzhen Institutes of Advanced Technology356b431d4f7a2a0a38cf971c84568207dcdbf189
wider_attributeWIDER AttributeHuman Attribute Recognition by Deep Hierarchical ContextsHuman Attribute Recognition by Deep Hierarchical Contexts[pdf][s2]Chinese University of Hong Kong44d23df380af207f5ac5b41459c722c87283e1eb
wider_faceWIDER FACEWIDER FACE: A Face Detection BenchmarkWIDER FACE: A Face Detection Benchmark[pdf][s2]Chinese University of Hong Kong52d7eb0fbc3522434c13cc247549f74bb9609c5d
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
wlfdbWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
youtube_facesYouTubeFacesFace Recognition in Unconstrained Videos with Matched Background SimilarityFace recognition in unconstrained videos with matched background similarity[pdf][s2]Open University of Israel560e0e58d0059259ddf86fcec1fa7975dee6a868
youtube_makeupYMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
youtube_makeupYMUAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
gfwYouTube PoseMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
\ No newline at end of file +discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]		833fa04463d90aab4a9fe2870d480f0b40df446e
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
usedUSED Social Event DatasetUSED: A Large-scale Social Event Detection DatasetUSED: a large-scale social event detection dataset[pdf][s2]8627f019882b024aef92e4eb9355c499c733e5b7
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
maflMAFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]Chinese University of Hong Kong8a3c5507237957d013a0fe0f082cab7f757af6ee
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningFacial Landmark Detection by Deep Multi-task Learning[pdf][s2]Chinese University of Hong Kong8a3c5507237957d013a0fe0f082cab7f757af6ee
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
v47V47Re-identification of Pedestrians with Variable Occlusion and ScaleRe-identification of pedestrians with variable occlusion and scale[pdf][s2]922e0a51a3b8c67c4c6ac09a577ff674cbd28b34
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
helenHelenInteractive Facial Feature LocalizationInteractive Facial Feature Localization[pdf][s2]University of Illinois, Urbana-Champaign95f12d27c3b4914e0668a268360948bce92f7db3
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
megafaceMegaFaceThe MegaFace Benchmark: 1 Million Faces for Recognition at ScaleThe MegaFace Benchmark: 1 Million Faces for Recognition at Scale[pdf][s2]University of Washington96e0cfcd81cdeb8282e29ef9ec9962b125f379b0
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)The M2VTS Multimodal Face Database (Release 1.00)[pdf][s2]9a9877791945c6fa4c1743ec6d3fb32570ef8481
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
facebook_100Facebook100Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
pubfig_83pubfig83Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on FacebookScaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook[pdf][s2]Harvard University9c23859ec7313f2e756a3e85575735e0c52249f4
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHi4D-ADSIP 3-D dynamic facial articulation database[pdf][s2]a8d0b149c2eadaa02204d3e4356fbc8eccf3b315
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
put_facePut FaceThe PUT face databaseThe put face database[pdf][s2]ae0aee03d946efffdc7af2362a42d3750e7dd48a
afew_vaAFEW-VACollecting Large, Richly Annotated Facial-Expression Databases from MoviesCollecting Large, Richly Annotated Facial-Expression Databases from Movies[pdf][s2]Australian National Universityb1f4423c227fa37b9680787be38857069247a307
ucf_101UCF101UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildUCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild[pdf][s2]University of Central Floridab5f2846a506fc417e7da43f6a7679146d99c5e96
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseXm2vtsdb: the Extended M2vts Database[pdf][s2]b62628ac06bbac998a3ab825324a41a11bc3a988
ifdbIFDBIranian Face Database with age, pose and expressionIranian Face Database with age, pose and expression[pdf][s2]b71d1aa90dcbe3638888725314c0d56640c1fef1
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
pornodbPornography DBPooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
scut_fbpSCUT-FBPSCUT-FBP: A Benchmark Dataset for Facial Beauty PerceptionSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception[pdf][s2]South China University of Technologybd26dabab576adb6af30484183c9c9c8379bf2e0
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf][s2]a5a3bc3e5e9753769163cb30b16dbd12e266b93e
mcgillMcGill Real WorldRobust Semi-automatic Head Pose Labeling for Real-World Face Video SequencesRobust semi-automatic head pose labeling for real-world face video sequences[pdf][s2]McGill Universityc570d1247e337f91e555c3be0e8c8a5aba539d9f
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
imfdbIMFDBIndian Movie Face Database: A Benchmark for Face Recognition Under Wide VariationsIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf][s2]ca3e88d87e1344d076c964ea89d91a75c417f5ee
bu_3dfeBU-3DFEA 3D Facial Expression Database For Facial Behavior ResearchA 3D facial expression database for facial behavior research[pdf][s2]SUNY Binghamtoncc589c499dcf323fe4a143bbef0074c3e31f9b60
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
megaageMegaAgeQuantifying Facial Age by Posterior of Age ComparisonsQuantifying Facial Age by Posterior of Age Comparisons[pdf][s2]Chinese University of Hong Kongd80a3d1f3a438e02a6685e66ee908446766fefa9
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
families_in_the_wildFIWVisual Kinship Recognition of Families in the WildVisual Kinship Recognition of Families in the Wild[pdf][s2]dd65f71dac86e36eecbd3ed225d016c3336b4a13
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
gfwYouTube PoseMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
visual_phrasesPhrasal RecognitionRecognition using Visual PhrasesRecognition using visual phrases[pdf][s2]e8de844fefd54541b71c9823416daa238be65546
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
vgg_faces2VGG Face2VGGFace2: A dataset for recognising faces across pose and ageVGGFace2: A Dataset for Recognising Faces across Pose and Age[pdf][s2]University of Oxfordeb027969f9310e0ae941e2adee2d42cdf07d938c
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
mug_facesMUG FacesThe MUG Facial Expression DatabaseThe MUG facial expression database[pdf][s2]Aristotle University of Thessalonikif1af714b92372c8e606485a3982eab2f16772ad8
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the ArtPedestrian Detection: An Evaluation of the State of the Art[pdf][s2]f72f6a45ee240cc99296a287ff725aaa7e7ebb35
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
miwMIWAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
youtube_makeupYMUAutomatic Facial Makeup Detection with Application in Face RecognitionAutomatic facial makeup detection with application in face recognition[pdf][s2]West Virginia Universityfcc6fe6007c322641796cb8792718641856a22a7
nd_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face RecognitionUsing a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf][s2]fd8168f1c50de85bac58a8d328df0a50248b16ae
\ No newline at end of file diff --git a/scraper/reports/paper_title_report_no_location.html b/scraper/reports/paper_title_report_no_location.html index 6eb38a75..66e93e87 100644 --- a/scraper/reports/paper_title_report_no_location.html +++ b/scraper/reports/paper_title_report_no_location.html @@ -1,3 +1,3 @@ -Papers with no location

Papers with no location

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
a_pascal_yahooaPascalDescribing Objects by their AttributesDescribing objects by their attributes[pdf][s2]2e384f057211426ac5922f1b33d2aa8df5d51f57
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
berkeley_poseBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
bjut_3dBJUT-3DThe BJUT-3D Large-Scale Chinese Face DatabaseA novel face recognition method based on 3D face model[pdf][s2]1ed1a49534ad8dd00f81939449f6389cfbc25321
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
brainwashBrainwashBrainwash datasetBrainwash: A Data System for Feature Engineering[pdf][s2]214c966d1f9c2a4b66f4535d9a0d4078e63a5867
buhmap_dbBUHMAP-DBFacial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
cafeCAFEThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the ArtPedestrian Detection: An Evaluation of the State of the Art[pdf][s2]f72f6a45ee240cc99296a287ff725aaa7e7ebb35
camelCAMELCAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and TrackingApplication of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis[pdf][s2]5801690199c1917fa58c35c3dead177c0b8f9f2d
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
cityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene UnderstandingThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf][s2]32cde90437ab5a70cf003ea36f66f2de0e24b3ab
cityscapesCityscapesThe Cityscapes DatasetThe Cityscapes Dataset[pdf][s2]5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expressionThe Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf][s2]4d9a02d080636e9666c4d1cc438b9893391ec6c7
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]06f02199690961ba52997cde1527e714d2b3bf8f
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
disfaDISFADISFA: A Spontaneous Facial Action Intensity DatabaseExtended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions[pdf][s2]a5acda0e8c0937bfed013e6382da127103e41395
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene AnalysisDepth and Appearance for Mobile Scene Analysis[pdf][s2]13f06b08f371ba8b5d31c3e288b4deb61335b462
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
families_in_the_wildFIWVisual Kinship Recognition of Families in the WildVisual Kinship Recognition of Families in the Wild[pdf][s2]dd65f71dac86e36eecbd3ed225d016c3336b4a13
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroGeneralização cartográfica automatizada para um banco de dados cadastral[pdf][s2]b6b1b0632eb9d4ab1427278f5e5c46f97753c73d
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
frav3dFRAV3DMULTIMODAL 2D, 2.5D & 3D FACE VERIFICATIONMultimodal 2D, 2.5D & 3D Face Verification[pdf][s2]2b926b3586399d028b46315d7d9fb9d879e4f79c
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
gallagherGallagherClothing Cosegmentation for Recognizing PeopleClothing cosegmentation for recognizing people[pdf][s2]22ad2c8c0f4d6aa4328b38d894b814ec22579761
gavab_dbGavabGavabDB: a 3D face databaseExpression invariant 3D face recognition with a Morphable Model[pdf][s2]42505464808dfb446f521fc6ff2cfeffd4d68ff1
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
grazGraz PedestrianGeneric Object Recognition with BoostingGeneric object recognition with boosting[pdf][s2]2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
grazGraz PedestrianObject Recognition Using Segmentation for Feature DetectionObject recognition using segmentation for feature detection[pdf][s2]12ad3b5bbbf407f8e54ea692c07633d1a867c566
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHigh-resolution comprehensive 3-D dynamic database for facial articulation analysis[pdf][s2]24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hrt_transgenderHRT TransgenderFace recognition across gender transformation using SVM ClassifierFace recognition: A literature survey[pdf][s2]28312c3a47c1be3a67365700744d3d6665b86f22
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ifdbIFDBIranian Face Database with age, pose and expressionIranian Face Database with age, pose and expression[pdf][s2]b71d1aa90dcbe3638888725314c0d56640c1fef1
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
ijb_cIJB-APushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
ijb_cIJB-BIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]21d9d0deed16f0ad62a4865e9acf0686f4f15492
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
imfdbIMFDBIndian Movie Face Database: A Benchmark for Face Recognition Under Wide VariationsIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf][s2]ca3e88d87e1344d076c964ea89d91a75c417f5ee
imm_faceIMM Face DatasetThe IMM Face Database - An Annotated Dataset of 240 Face ImagesAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
inria_personINRIA PedestrianHistograms of Oriented Gradients for Human DetectionHistograms of oriented gradients for human detection[pdf][s2]10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
kdefKDEFThe Karolinska Directed Emotional Faces – KDEFGaze fixation and the neural circuitry of face processing in autism[pdf][s2]93884e46c49f7ae1c7c34046fbc28882f2bd6341
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship Verification through Transfer Learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
kinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face RecognitionKinectFaceDB: A Kinect Database for Face Recognition[pdf][s2]0b440695c822a8e35184fb2f60dcdaa8a6de84ae
kittiKITTIVision meets Robotics: The KITTI DatasetThe Role of Machine Vision for Intelligent Vehicles[pdf][s2]35ba4ebfd017a56b51e967105af9ae273c9b0178
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning Effective Human Pose Estimation from Inaccurate AnnotationLearning effective human pose estimation from inaccurate annotation[pdf][s2]4e4746094bf60ee83e40d8597a6191e463b57f76
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfw_aLFW-aEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
motMOTLearning to associate: HybridBoosted multi-target tracker for crowded sceneLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
muctMUCTThe MUCT Landmarked Face DatabaseAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
multi_pieMULTIPIEMulti-PIEScheduling heterogeneous multi-cores through performance impact estimation (PIE)[pdf][s2]109df0e8e5969ddf01e073143e83599228a1163f
names_and_faces_newsNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nd_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face RecognitionUsing a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf][s2]fd8168f1c50de85bac58a8d328df0a50248b16ae
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]7f4040b482d16354d5938c1d1b926b544652bf5b
nudedetectionNude DetectionA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classi cationSummary of Research on Informant Accuracy in Network Data, 11 and on the Reverse Small World Problem[pdf][s2]fb82681ac5d3487bd8e52dbb3d1fa220eeac855e
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pornodbPornography DBPooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
put_facePut FaceThe PUT face databaseLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
qmul_gridGRIDMulti-Camera Activity Correlation AnalysisMulti-camera activity correlation analysis[pdf][s2]3b5b6d19d4733ab606c39c69a889f9e67967f151
qmul_gridGRIDTime-delayed correlation analysis for multi-camera activity understandingTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf][s2]2edb87494278ad11641b6cf7a3f8996de12b8e14
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
scfaceSCfaceSCface – surveillance cameras face databaseLarge Variability Surveillance Camera Face Database[pdf][s2]f3b84a03985de3890b400b68e2a92c0a00afd9d0
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
sheffieldSheffield FaceFace Recognition: From Theory to ApplicationsFace Description with Local Binary Patterns: Application to Face Recognition[pdf][s2]3607afdb204de9a5a9300ae98aa4635d9effcda2
social_relationSocial RelationFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
sun_attributesSUNSUN Attribute Database: +Papers with no location

Papers with no location

keynameour titlefound titleaddresss2 id
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
buhmap_dbBUHMAP-DBFacial Feature Tracking and Expression Recognition for Sign LanguageFacial feature tracking and expression recognition for sign language[pdf][s2]014b8df0180f33b9fea98f34ae611c6447d761d2
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
kittiKITTIVision meets Robotics: The KITTI DatasetVision meets robotics: The KITTI dataset[pdf][s2]026e3363b7f76b51cc711886597a44d5f1fd1de2
ilids_mctsi-LIDSImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
psuPSUVision-based Analysis of Small Groups in Pedestrian CrowdsVision-Based Analysis of Small Groups in Pedestrian Crowds[pdf][s2]066000d44d6691d27202896691f08b27117918b9
ifdbIFDBIranian Face Database and Evaluation with a New Detection AlgorithmIranian Face Database and Evaluation with a New Detection Algorithm[pdf][s2]066d71fcd997033dce4ca58df924397dfe0b5fd1
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]06f02199690961ba52997cde1527e714d2b3bf8f
mit_cbclMIT CBCLComponent-based Face Recognition with 3D Morphable ModelsComponent-Based Face Recognition with 3D Morphable Models[pdf][s2]079a0a3bf5200994e1f972b1b9197bf2f90e87d4
kin_faceUB KinFaceUnderstanding Kin Relationships in a PhotoUnderstanding Kin Relationships in a Photo[pdf][s2]08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7
raidRAiDConsistent Re-identification in a Camera NetworkConsistent Re-identification in a Camera Network[pdf][s2]09d78009687bec46e70efcf39d4612822e61cb8c
pipaPIPABeyond Frontal Faces: Improving Person Recognition Using Multiple CuesBeyond frontal faces: Improving Person Recognition using multiple cues[pdf][s2]0a85bdff552615643dd74646ac881862a7c7072d
kinectfaceKinectFaceDBKinectFaceDB: A Kinect Database for Face RecognitionKinectFaceDB: A Kinect Database for Face Recognition[pdf][s2]0b440695c822a8e35184fb2f60dcdaa8a6de84ae
feretFERETThe FERET Verification Testing Protocol for Face Recognition AlgorithmsThe FERET Verification Testing Protocol for Face Recognition Algorithms[pdf][s2]0c4a139bb87c6743c7905b29a3cfec27a5130652
grazGraz PedestrianWeak Hypotheses and Boosting for Generic Object Detection and RecognitionWeak Hypotheses and Boosting for Generic Object Detection and Recognition[pdf][s2]0c91808994a250d7be332400a534a9291ca3b60e
ijb_cIJB-BIARPA Janus Benchmark-B Face DatasetIARPA Janus Benchmark-B Face Dataset[pdf][s2]0cb2dd5f178e3a297a0c33068961018659d0f443
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
hollywood_headsetHollywoodHeadsContext-aware CNNs for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
lagLAGLarge Age-Gap Face Verification by Feature Injection in Deep NetworksLarge age-gap face verification by feature injection in deep networks[pdf][s2]0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e
stickmen_familyWe Are Family StickmenWe Are Family: Joint Pose Estimation of Multiple PersonsWe Are Family: Joint Pose Estimation of Multiple Persons[pdf][s2]0dc11a37cadda92886c56a6fb5191ded62099c28
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
feretFERETThe FERET Evaluation Methodology for Face-Recognition AlgorithmsThe FERET Evaluation Methodology for Face-Recognition Algorithms[pdf][s2]0f0fcf041559703998abf310e56f8a2f90ee6f21
imdb_wikiIMDBDeep expectation of real and apparent age from a single image without facial landmarksDeep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks[pdf][s2]10195a163ab6348eef37213a46f60a3d87f289c5
inria_personINRIA PedestrianHistograms of Oriented Gradients for Human DetectionHistograms of oriented gradients for human detection[pdf][s2]10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5
grazGraz PedestrianObject Recognition Using Segmentation for Feature DetectionObject recognition using segmentation for feature detection[pdf][s2]12ad3b5bbbf407f8e54ea692c07633d1a867c566
lfw_aLFW-aEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background StatisticsEffective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics[pdf][s2]133f01aec1534604d184d56de866a4bd531dac87
eth_andreas_essETHZ PedestrianDepth and Appearance for Mobile Scene AnalysisDepth and Appearance for Mobile Scene Analysis[pdf][s2]13f06b08f371ba8b5d31c3e288b4deb61335b462
ijb_cIJB-APushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark APushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A[pdf][s2]140c95e53c619eac594d70f6369f518adfea12ef
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
pridPRIDPerson Re-Identification by Descriptive and Discriminative ClassificationPerson Re-identification by Descriptive and Discriminative Classification[pdf][s2]16c7c31a7553d99f1837fc6e88e77b5ccbb346b8
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
deep_fashionDeepFashionDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich AnnotationsDeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations[pdf][s2]18010284894ed0edcca74e5bf768ee2e15ef7841
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification[pdf][s2]18858cc936947fc96b5c06bbe3c6c2faa5614540
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
jpl_poseJPL-Interaction datasetFirst-Person Activity Recognition: What Are They Doing to Me?First-Person Activity Recognition: What Are They Doing to Me?[pdf][s2]1aad2da473888cb7ebc1bfaa15bfa0f1502ce005
adienceAdienceAge and Gender Estimation of Unfiltered FacesAge and Gender Estimation of Unfiltered Faces[pdf][s2]1be498d4bbc30c3bfd0029114c784bc2114d67c0
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
caltech_pedestriansCaltech PedestriansPedestrian Detection: A BenchmarkPedestrian detection: A benchmark[pdf][s2]1dc35905a1deff8bc74688f2d7e2f48fd2273275
immediacyImmediacyMulti-task Recurrent Neural Network for Immediacy PredictionMulti-task Recurrent Neural Network for Immediacy Prediction[pdf][s2]1e3df3ca8feab0b36fd293fe689f93bb2aaac591
cafeCAFEThe Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adultsThe Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults[pdf][s2]20388099cc415c772926e47bcbbe554e133343d1
bbc_poseBBC PoseAutomatic and Efficient Human Pose Estimation for Sign Language VideosAutomatic and Efficient Human Pose Estimation for Sign Language Videos[pdf][s2]213a579af9e4f57f071b884aa872651372b661fd
3d_rma3D-RMAAutomatic 3D Face AuthenticationAutomatic 3D face authentication[pdf][s2]2160788824c4c29ffe213b2cbeb3f52972d73f37
large_scale_person_searchLarge Scale Person SearchEnd-to-End Deep Learning for Person SearchEnd-to-End Deep Learning for Person Search[pdf][s2]2161f6b7ee3c0acc81603b01dc0df689683577b9
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]21d9d0deed16f0ad62a4865e9acf0686f4f15492
rap_pedestrianRAPA Richly Annotated Dataset for Pedestrian Attribute RecognitionA Richly Annotated Dataset for Pedestrian Attribute Recognition[pdf][s2]221c18238b829c12b911706947ab38fd017acef7
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsEvaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics[pdf][s2]2258e01865367018ed6f4262c880df85b94959f8
saivtSAIVT SoftBioA Database for Person Re-Identification in Multi-Camera Surveillance NetworksA Database for Person Re-Identification in Multi-Camera Surveillance Networks[pdf][s2]22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b
petsPETS 2017PETS 2017: Dataset and ChallengePETS 2017: Dataset and Challenge[pdf][s2]22909dd19a0ec3b6065334cb5be5392cb24d839d
gallagherGallagherClothing Cosegmentation for Recognizing PeopleClothing cosegmentation for recognizing people[pdf][s2]22ad2c8c0f4d6aa4328b38d894b814ec22579761
expwExpWFrom Facial Expression Recognition to Interpersonal Relation PredictionFrom Facial Expression Recognition to Interpersonal Relation Prediction[pdf][s2]22f656d0f8426c84a33a267977f511f127bfd7f3
cas_pealCAS-PEALThe CAS-PEAL Large-Scale Chinese Face Database and Baseline EvaluationsThe CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
faceplaceFace PlaceRecognizing disguised facesRecognizing disguised faces[pdf][s2]25474c21613607f6bb7687a281d5f9d4ffa1f9f3
afew_vaAFEW-VAAFEW-VA database for valence and arousal estimation in-the-wildAFEW-VA database for valence and arousal estimation in-the-wild[pdf][s2]2624d84503bc2f8e190e061c5480b6aa4d89277a
cofwCOFWRobust face landmark estimation under occlusionRobust Face Landmark Estimation under Occlusion[pdf][s2]2724ba85ec4a66de18da33925e537f3902f21249
duke_mtmcDuke MTMCPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingPerformance Measures and a Data Set for Multi-Target, Multi-Camera Tracking[pdf][s2]27a2fad58dd8727e280f97036e0d2bc55ef5424c
h3dH3DPoselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsPoselets: Body part detectors trained using 3D human pose annotations[pdf][s2]2830fb5282de23d7784b4b4bc37065d27839a412
megafaceMegaFaceLevel Playing Field for Million Scale Face RecognitionLevel Playing Field for Million Scale Face Recognition[pdf][s2]28d4e027c7e90b51b7d8908fce68128d1964668a
mscelebMsCelebMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face RecognitionMS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[pdf][s2]291265db88023e92bb8c8e6390438e5da148e8f5
ferplusFER+Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label DistributionTraining deep networks for facial expression recognition with crowd-sourced label distribution[pdf][s2]298cbc3dfbbb3a20af4eed97906650a4ea1c29e0
scfaceSCfaceSCface – surveillance cameras face databaseSCface – surveillance cameras face database[pdf][s2]29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d
petaPETAPedestrian Attribute Recognition At Far DistancePedestrian Attribute Recognition At Far Distance[pdf][s2]2a4bbee0b4cf52d5aadbbc662164f7efba89566c
mmi_facial_expressionMMI Facial Expression DatasetWEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSISWeb-based database for facial expression analysis[pdf][s2]2a75f34663a60ab1b04a0049ed1d14335129e908
bosphorusThe BosphorusBosphorus Database for 3D Face AnalysisBosphorus Database for 3D Face Analysis[pdf][s2]2acf7e58f0a526b957be2099c10aab693f795973
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
frav3dFRAV3DMULTIMODAL 2D, 2.5D & 3D FACE VERIFICATIONMultimodal 2D, 2.5D & 3D Face Verification[pdf][s2]2b926b3586399d028b46315d7d9fb9d879e4f79c
clothing_co_parsingCCPClothing Co-Parsing by Joint Image Segmentation and LabelingClothing Co-parsing by Joint Image Segmentation and Labeling[pdf][s2]2bf8541199728262f78d4dced6fb91479b39b738
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
a_pascal_yahooaPascalDescribing Objects by their AttributesDescribing objects by their attributes[pdf][s2]2e384f057211426ac5922f1b33d2aa8df5d51f57
3dpes3DPeS3DPes: 3D People Dataset for Surveillance and Forensics3DPeS: 3D people dataset for surveillance and forensics[pdf][s2]2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e
qmul_gridGRIDTime-delayed correlation analysis for multi-camera activity understandingTime-Delayed Correlation Analysis for Multi-Camera Activity Understanding[pdf][s2]2edb87494278ad11641b6cf7a3f8996de12b8e14
grazGraz PedestrianGeneric Object Recognition with BoostingGeneric object recognition with boosting[pdf][s2]2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9
names_and_faces_newsNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
feretFERETFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test ResultsFERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results[pdf][s2]31de9b3dd6106ce6eec9a35991b2b9083395fd0b
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
cityscapesCityscapesThe Cityscapes Dataset for Semantic Urban Scene UnderstandingThe Cityscapes Dataset for Semantic Urban Scene Understanding[pdf][s2]32cde90437ab5a70cf003ea36f66f2de0e24b3ab
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
mpii_human_poseMPII Human Pose2D Human Pose Estimation: New Benchmark and State of the Art Analysis2D Human Pose Estimation: New Benchmark and State of the Art Analysis[pdf][s2]3325860c0c82a93b2eac654f5324dd6a776f609e
penn_fudanPenn FudanObject Detection Combining Recognition and SegmentationObject Detection Combining Recognition and Segmentation[pdf][s2]3394168ff0719b03ff65bcea35336a76b21fe5e4
coco_qaCOCO QAExploring Models and Data for Image Question AnsweringExploring Models and Data for Image Question Answering[pdf][s2]35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
rafdRaFDPresentation and validation of the Radboud Faces DatabasePresentation and validation of the Radboud Faces Database[pdf][s2]3765df816dc5a061bc261e190acc8bdd9d47bec0
vmuVMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
youtube_makeupYMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
cuhk02CUHK02Locally Aligned Feature Transforms across ViewsLocally Aligned Feature Transforms across Views[pdf][s2]38b55d95189c5e69cf4ab45098a48fba407609b4
sdu_vidSDU-VIDA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-IdentificationA Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification[pdf][s2]3b4ec8af470948a72a6ed37a9fd226719a874ebc
qmul_gridGRIDMulti-Camera Activity Correlation AnalysisMulti-camera activity correlation analysis[pdf][s2]3b5b6d19d4733ab606c39c69a889f9e67967f151
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
bio_idBioID FaceRobust Face Detection Using the Hausdorff DistanceRobust Face Detection Using the Hausdorff Distance[pdf][s2]4053e3423fb70ad9140ca89351df49675197196a
distance_nighttimeLong Distance Heterogeneous FaceNighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral MatchingNighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching[pdf][s2]4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06
market_1501Market 1501Scalable Person Re-identification: A BenchmarkScalable Person Re-identification: A Benchmark[pdf][s2]4308bd8c28e37e2ed9a3fcfe74d5436cce34b410
tud_multiviewTUD-MultiviewMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tud_stadtmitteTUD-StadtmitteMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
cuhk01CUHK01Human Reidentification with Transferred Metric LearningHuman Reidentification with Transferred Metric Learning[pdf][s2]44484d2866f222bbb9b6b0870890f9eea1ffb2d0
vadanaVADANAVADANA: A dense dataset for facial image analysisVADANA: A dense dataset for facial image analysis[pdf][s2]4563b46d42079242f06567b3f2e2f7a80cb3befe
sdu_vidSDU-VIDLocal descriptors encoded by Fisher vectors for person re-identificationLocal Descriptors Encoded by Fisher Vectors for Person Re-identification[pdf][s2]46a01565e6afe7c074affb752e7069ee3bf2e4ef
fiaCMU FiAThe CMU Face In Action (FIA) DatabaseThe CMU Face In Action (FIA) Database[pdf][s2]47662d1a368daf70ba70ef2d59eb6209f98b675d
kin_faceUB KinFaceKinship Verification through Transfer LearningKinship Verification through Transfer Learning[pdf][s2]4793f11fbca4a7dba898b9fff68f70d868e2497c
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
apisAPiS1.0Pedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
coco_actionCOCO-aDescribing Common Human Visual Actions in ImagesDescribing Common Human Visual Actions in Images[pdf][s2]4946ba10a4d5a7d0a38372f23e6622bd347ae273
leeds_sports_poseLeeds Sports PoseClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
stickmen_buffyBuffy StickmenClustered Pose and Nonlinear Appearance Models for Human Pose EstimationClustered Pose and Nonlinear Appearance Models for Human Pose Estimation[pdf][s2]4b1d23d17476fcf78f4cbadf69fb130b1aa627c0
czech_news_agencyUFIUnconstrained Facial Images: Database for Face Recognition under Real-world ConditionsUnconstrained Facial Images: Database for Face Recognition Under Real-World Conditions[pdf][s2]4b4106614c1d553365bad75d7866bff0de6056ed
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
cohn_kanade_plusCK+The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expressionThe Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression[pdf][s2]4d9a02d080636e9666c4d1cc438b9893391ec6c7
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
leeds_sports_pose_extendedLeeds Sports Pose ExtendedLearning Effective Human Pose Estimation from Inaccurate AnnotationLearning effective human pose estimation from inaccurate annotation[pdf][s2]4e4746094bf60ee83e40d8597a6191e463b57f76
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
sotonSOTON HiDOn a Large Sequence-Based Human Gait DatabaseOn a large sequence-based human gait database[pdf][s2]4f93cd09785c6e77bf4bc5a788e079df524c8d21
violent_flowsViolent FlowsViolent Flows: Real-Time Detection of Violent Crowd BehaviorViolent flows: Real-time detection of violent crowd behavior[pdf][s2]5194cbd51f9769ab25260446b4fa17204752e799
bp4d_plusBP4D+Multimodal Spontaneous Emotion Corpus for Human Behavior AnalysisMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis[pdf][s2]53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4
reseedReSEEDReSEED: Social Event dEtection DatasetReSEED: social event dEtection dataset[pdf][s2]54983972aafc8e149259d913524581357b0f91c3
orlORLParameterisation of a Stochastic Model for Human Face IdentificationParameterisation of a stochastic model for human face identification[pdf][s2]55206f0b5f57ce17358999145506cd01e570358c
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
data_61Data61 PedestrianA Multi-Modal Graphical Model for Scene AnalysisA Multi-modal Graphical Model for Scene Analysis[pdf][s2]563c940054e4b456661762c1ab858e6f730c3159
unbc_shoulder_painUNBC-McMaster PainPAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive DatabasePainful data: The UNBC-McMaster shoulder pain expression archive database[pdf][s2]56ffa7d906b08d02d6d5a12c7377a57e24ef3391
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
50_people_one_question50 People One QuestionMerging Pose Estimates Across Space and TimeMerging Pose Estimates Across Space and Time[pdf][s2]5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
motMOTLearning to associate: HybridBoosted multi-target tracker for crowded sceneLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
disfaDISFADISFA: A Spontaneous Facial Action Intensity DatabaseDISFA: A Spontaneous Facial Action Intensity Database[pdf][s2]5a5f0287484f0d480fed1ce585dbf729586f0edc
wlfdbWLFDBWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
cocoCOCOMicrosoft COCO: Common Objects in ContextMicrosoft COCO: Common Objects in Context[pdf][s2]5e0f8c355a37a5a89351c02f174e7a5ddcb98683
cityscapesCityscapesThe Cityscapes DatasetThe Cityscapes Dataset[pdf][s2]5ffd74d2873b7cba2cbc5fd295cc7fbdedca22a2
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
caltech_10k_web_facesCaltech 10K Web FacesPruning Training Sets for Learning of Object CategoriesPruning training sets for learning of object categories[pdf][s2]636b8ffc09b1b23ff714ac8350bb35635e49fa3c
bfmBFMA 3D Face Model for Pose and Illumination Invariant Face RecognitionA 3D Face Model for Pose and Illumination Invariant Face Recognition[pdf][s2]639937b3a1b8bded3f7e9a40e85bd3770016cf3c
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
complex_activitiesOngoing Complex ActivitiesRecognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label SpaceRecognition of ongoing complex activities by sequence prediction over a hierarchical label space[pdf][s2]65355cbb581a219bd7461d48b3afd115263ea760
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
sun_attributesSUNThe SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingThe SUN Attribute Database: Beyond Categories for Deeper Scene Understanding[pdf][s2]66e6f08873325d37e0ec20a4769ce881e04e964e
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
cuhk03CUHK03DeepReID: Deep Filter Pairing Neural Network for Person Re-identificationDeepReID: Deep Filter Pairing Neural Network for Person Re-identification[pdf][s2]6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3
ar_facedbAR FaceThe AR Face DatabaseThe AR face database[pdf][s2]6d96f946aaabc734af7fe3fc4454cf8547fcd5ed
agedbAgeDBAgeDB: the first manually collected, in-the-wild age databaseAgeDB: The First Manually Collected, In-the-Wild Age Database[pdf][s2]6dcf418c778f528b5792104760f1fbfe90c6dd6a
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
affectnetAffectNetAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the WildAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild[pdf][s2]758d7e1be64cc668c59ef33ba8882c8597406e53
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
berkeley_poseBPADDescribing People: A Poselet-Based Approach to Attribute ClassificationDescribing people: A poselet-based approach to attribute classification[pdf][s2]7808937b46acad36e43c30ae4e9f3fd57462853d
mapillaryMapillaryThe Mapillary Vistas Dataset for Semantic Understanding of Street ScenesThe Mapillary Vistas Dataset for Semantic Understanding of Street Scenes[pdf][s2]79828e6e9f137a583082b8b5a9dfce0c301989b8
nudedetectionNude DetectionA Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude DetectionA bag-of-features approach based on Hue-SIFT descriptor for nude detection[pdf][s2]7ace44190729927e5cb0dd5d363fcae966fe13f7
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]7f4040b482d16354d5938c1d1b926b544652bf5b
sun_attributesSUNSUN Attribute Database: Discovering, Annotating, and Recognizing Scene AttributesSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf][s2]833fa04463d90aab4a9fe2870d480f0b40df446e
svsSVSPedestrian Attribute Classification in Surveillance: Database and EvaluationPedestrian Attribute Classification in Surveillance: Database and Evaluation[pdf][s2]488e475eeb3bb39a145f23ede197cd3620f1d98a
texas_3dfrdTexas 3DFRDTexas 3D Face Recognition DatabaseTexas 3D Face Recognition Database[pdf][s2]4d58f886f5150b2d5e48fd1b5a49e09799bf895d
texas_3dfrdTexas 3DFRDAnthropometric 3D Face RecognitionAnthropometric 3D Face Recognition[pdf][s2]2ce2560cf59db59ce313bbeb004e8ce55c5ce928
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
tud_brusselsTUD-BrusselsMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_campusTUD-CampusPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_crossingTUD-CrossingPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_motionpairsTUD-MotionparisMulti-Cue Onboard Pedestrian DetectionMulti-cue onboard pedestrian detection[pdf][s2]6ad5a38df8dd4cdddd74f31996ce096d41219f72
tud_multiviewTUD-MultiviewMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tud_pedestrianTUD-PedestrianPeople-Tracking-by-Detection and People-Detection-by-TrackingPeople-tracking-by-detection and people-detection-by-tracking[pdf][s2]3316521a5527c7700af8ae6aef32a79a8b83672c
tud_stadtmitteTUD-StadtmitteMonocular 3D Pose Estimation and Tracking by DetectionMonocular 3D pose estimation and tracking by detection[pdf][s2]436f798d1a4e54e5947c1e7d7375c31b2bdb4064
tvhiTVHIHigh Five: Recognising human interactions in TV showsHigh Five: Recognising human interactions in TV shows[pdf][s2]3cd40bfa1ff193a96bde0207e5140a399476466c
ucf_crowdUCF-CC-50Multi-Source Multi-Scale Counting in Extremely Dense Crowd ImagesMulti-source Multi-scale Counting in Extremely Dense Crowd Images[pdf][s2]32c801cb7fbeb742edfd94cccfca4934baec71da
ucf_selfieUCF SelfieHow to Take a Good Selfie?How to Take a Good Selfie?[pdf][s2]041d3eedf5e45ce5c5229f0181c5c576ed1fafd6
umbUMBUMB-DB: A Database of Partially Occluded 3D FacesUMB-DB: A database of partially occluded 3D faces[pdf][s2]16e8b0a1e8451d5f697b94c0c2b32a00abee1d52
umd_facesUMDUMDFaces: An Annotated Face Dataset for Training Deep NetworksUMDFaces: An annotated face dataset for training deep networks[pdf][s2]31b05f65405534a696a847dd19c621b7b8588263
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
unbc_shoulder_painUNBC-McMaster PainPAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive DatabasePainful data: The UNBC-McMaster shoulder pain expression archive database[pdf][s2]56ffa7d906b08d02d6d5a12c7377a57e24ef3391
usedUSED Social Event DatasetUSED: A Large-scale Social Event Detection DatasetUSED: a large-scale social event detection dataset[pdf][s2]8627f019882b024aef92e4eb9355c499c733e5b7
v47V47Re-identification of Pedestrians with Variable Occlusion and ScaleRe-identification of pedestrians with variable occlusion and scale[pdf][s2]922e0a51a3b8c67c4c6ac09a577ff674cbd28b34
vadanaVADANAVADANA: A dense dataset for facial image analysisVADANA: A dense dataset for facial image analysis[pdf][s2]4563b46d42079242f06567b3f2e2f7a80cb3befe
vgg_facesVGG FaceDeep Face RecognitionDeep Face Recognition[pdf][s2]162ea969d1929ed180cc6de9f0bf116993ff6e06
violent_flowsViolent FlowsViolent Flows: Real-Time Detection of Violent Crowd BehaviorViolent flows: Real-time detection of violent crowd behavior[pdf][s2]5194cbd51f9769ab25260446b4fa17204752e799
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
visual_phrasesPhrasal RecognitionRecognition using Visual PhrasesRecognition using visual phrases[pdf][s2]e8de844fefd54541b71c9823416daa238be65546
vmuVMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
vocVOCThe PASCAL Visual Object Classes (VOC) ChallengeThe Pascal Visual Object Classes (VOC) Challenge[pdf][s2]0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a
vqaVQAVQA: Visual Question AnsweringVQA: Visual Question Answering[pdf][s2]01959ef569f74c286956024866c1d107099199f7
wardWARDRe-identify people in wide area camera networkRe-identify people in wide area camera network[pdf][s2]6f3c76b7c0bd8e1d122c6ea808a271fd4749c951
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
wlfdbWLFDB: Weakly Labeled Face DatabasesWLFDB: Weakly Labeled Face Databases[pdf][s2]5ad4e9f947c1653c247d418f05dad758a3f9277b
yale_facesYaleFacesAcquiring Linear Subspaces for Face Recognition under Variable LightingAcquiring linear subspaces for face recognition under variable lighting[pdf][s2]2ad0ee93d029e790ebb50574f403a09854b65b7e
yale_facesYaleFacesFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseFrom Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[pdf][s2]18c72175ddbb7d5956d180b65a96005c100f6014
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
yfcc_100mYFCC100MYFCC100M: The New Data in Multimedia ResearchYFCC100M: the new data in multimedia research[pdf][s2]010f0f4929e6a6644fb01f0e43820f91d0fad292
york_3dUOY 3D Face DatabaseThree-Dimensional Face Recognition: An Eigensurface ApproachThree-dimensional face recognition: an eigensurface approach[pdf][s2]19d1b811df60f86cbd5e04a094b07f32fff7a32a
youtube_makeupYMUCan Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?Can facial cosmetics affect the matching accuracy of face recognition systems?[pdf][s2]37d6f0eb074d207b53885bd2eb78ccc8a04be597
youtube_posesYouTube PosePersonalizing Human Video Pose EstimationPersonalizing Human Video Pose Estimation[pdf][s2]1c2802c2199b6d15ecefe7ba0c39bfe44363de38
gfwYouTube PoseMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
\ No newline at end of file +discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]		833fa04463d90aab4a9fe2870d480f0b40df446e
imdb_wikiIMDBDEX: Deep EXpectation of apparent age from a single imageDEX: Deep EXpectation of Apparent Age from a Single Image[pdf][s2]8355d095d3534ef511a9af68a3b2893339e3f96b
awe_earsAWE EarsEar Recognition: More Than a SurveyEar Recognition: More Than a Survey[pdf][s2]84fe5b4ac805af63206012d29523a1e033bc827e
casia_webfaceCASIA WebfaceLearning Face Representation from ScratchLearning Face Representation from Scratch[pdf][s2]853bd61bc48a431b9b1c7cab10c603830c488e39
usedUSED Social Event DatasetUSED: A Large-scale Social Event Detection DatasetUSED: a large-scale social event detection dataset[pdf][s2]8627f019882b024aef92e4eb9355c499c733e5b7
tiny_facesTinyFaceLow-Resolution Face RecognitionLow-Resolution Face Recognition[pdf][s2]8990cdce3f917dad622e43e033db686b354d057c
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
chalearnChaLearnChaLearn Looking at People: A Review of Events and ResourcesChaLearn looking at people: A review of events and resources[pdf][s2]8d5998cd984e7cce307da7d46f155f9db99c6590
hda_plusHDA+The HDA+ data set for research on fully automated re-identification systemsThe HDA+ Data Set for Research on Fully Automated Re-identification Systems[pdf][s2]8f02ec0be21461fbcedf51d864f944cfc42c875f
morphMORPH CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
morph_ncMORPH Non-CommercialMORPH: A Longitudinal Image Database of Normal Adult Age-ProgressionMORPH: a longitudinal image database of normal adult age-progression[pdf][s2]9055b155cbabdce3b98e16e5ac9c0edf00f9552f
v47V47Re-identification of Pedestrians with Variable Occlusion and ScaleRe-identification of pedestrians with variable occlusion and scale[pdf][s2]922e0a51a3b8c67c4c6ac09a577ff674cbd28b34
towncenterTownCenterStable Multi-Target Tracking in Real-Time Surveillance VideoStable multi-target tracking in real-time surveillance video[pdf][s2]9361b784e73e9238d5cefbea5ac40d35d1e3103f
4dfab4DFAB4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications[pdf][s2]9696ad8b164f5e10fcfe23aacf74bd6168aebb15
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)The M2VTS Multimodal Face Database (Release 1.00)[pdf][s2]9a9877791945c6fa4c1743ec6d3fb32570ef8481
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
precariousPrecariousExpecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial ImpostersExpecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters[pdf][s2]9e5378e7b336c89735d3bb15cf67eff96f86d39a
maflMAFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
mtflMTFLLearning Deep Representation for Face Alignment with Auxiliary AttributesLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
moments_in_timeMoments in TimeMoments in Time Dataset: one million videos for event understandingMoments in Time Dataset: one million videos for event understanding[pdf][s2]a5a44a32a91474f00a3cda671a802e87c899fbb4
aflwAFLWAnnotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark LocalizationAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
market1203Market 1203Orientation Driven Bag of Appearances for Person Re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
pku_reidPKU-ReidOrientation driven bag of appearances for person re-identificationOrientation Driven Bag of Appearances for Person Re-identification[pdf][s2]a7fe834a0af614ce6b50dc093132b031dd9a856b
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHi4D-ADSIP 3-D dynamic facial articulation database[pdf][s2]a8d0b149c2eadaa02204d3e4356fbc8eccf3b315
yawddYawDDYawDD: A Yawning Detection DatasetYawDD: a yawning detection dataset[pdf][s2]a94cae786d515d3450d48267e12ca954aab791c4
mrp_droneMRP DroneInvestigating Open-World Person Re-identification Using a DroneInvestigating Open-World Person Re-identification Using a Drone[pdf][s2]ad01687649d95cd5b56d7399a9603c4b8e2217d7
put_facePut FaceThe PUT face databaseThe put face database[pdf][s2]ae0aee03d946efffdc7af2362a42d3750e7dd48a
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseXm2vtsdb: the Extended M2vts Database[pdf][s2]b62628ac06bbac998a3ab825324a41a11bc3a988
ifdbIFDBIranian Face Database with age, pose and expressionIranian Face Database with age, pose and expression[pdf][s2]b71d1aa90dcbe3638888725314c0d56640c1fef1
pornodbPornography DBPooling in Image Representation: the Visual Codeword Point of ViewPooling in image representation: The visual codeword point of view[pdf][s2]b92a1ed9622b8268ae3ac9090e25789fc41cc9b8
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
marsMARSMARS: A Video Benchmark for Large-Scale Person Re-identificationMARS: A Video Benchmark for Large-Scale Person Re-Identification[pdf][s2]c0387e788a52f10bf35d4d50659cfa515d89fbec
nova_emotionsNovaemötions DatasetCrowdsourcing facial expressions for affective-interactionCrowdsourcing facial expressions for affective-interaction[pdf][s2]c06b13d0ec3f5c43e2782cd22542588e233733c3
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosHierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos[pdf][s2]a5a3bc3e5e9753769163cb30b16dbd12e266b93e
qmul_surv_faceQMUL-SurvFaceSurveillance Face Recognition ChallengeSurveillance Face Recognition Challenge[pdf][s2]c866a2afc871910e3282fd9498dce4ab20f6a332
emotio_netEmotioNet DatabaseEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the WildEmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild[pdf][s2]c900e0ad4c95948baaf0acd8449fde26f9b4952a
imfdbIMFDBIndian Movie Face Database: A Benchmark for Face Recognition Under Wide VariationsIndian Movie Face Database: A benchmark for face recognition under wide variations[pdf][s2]ca3e88d87e1344d076c964ea89d91a75c417f5ee
b3d_acB3D(AC)A 3-D Audio-Visual Corpus of Affective CommunicationA 3-D Audio-Visual Corpus of Affective Communication[pdf][s2]d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae
jiku_mobileJiku Mobile Video DatasetThe Jiku Mobile Video DatasetThe jiku mobile video dataset[pdf][s2]d178cde92ab3dc0dd2ebee5a76a33d556c39448b
stair_actionsSTAIR ActionSTAIR Actions: A Video Dataset of Everyday Home ActionsSTAIR Actions: A Video Dataset of Everyday Home Actions[pdf][s2]d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9
feretFERETThe FERET database and evaluation procedure for face-recognition algorithmsThe FERET database and evaluation procedure for face-recognition algorithms[pdf][s2]dc8b25e35a3acb812beb499844734081722319b4
families_in_the_wildFIWVisual Kinship Recognition of Families in the WildVisual Kinship Recognition of Families in the Wild[pdf][s2]dd65f71dac86e36eecbd3ed225d016c3336b4a13
scut_headSCUT HEADDetecting Heads using Feature Refine Net and Cascaded Multi-scale ArchitectureDetecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture[pdf][s2]d3200d49a19a4a4e4e9745ee39649b65d80c834b
sarc3dSarc3DSARC3D: a new 3D body model for People Tracking and Re-identificationSARC3D: A New 3D Body Model for People Tracking and Re-identification[pdf][s2]e27ef52c641c2b5100a1b34fd0b819e84a31b4df
fiw_300300-W300 faces In-the-wild challenge: Database and results300 Faces In-The-Wild Challenge: database and results[pdf][s2]e4754afaa15b1b53e70743880484b8d0736990ff
gfwYouTube PoseMerge or Not? Learning to Group Faces via Imitation LearningMerge or Not? Learning to Group Faces via Imitation Learning[pdf][s2]e58dd160a76349d46f881bd6ddbc2921f08d1050
visual_phrasesPhrasal RecognitionRecognition using Visual PhrasesRecognition using visual phrases[pdf][s2]e8de844fefd54541b71c9823416daa238be65546
mpi_largeLarge MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
mpi_smallSmall MPI Facial ExpressionThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsThe MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions[pdf][s2]ea050801199f98a1c7c1df6769f23f658299a3ae
msmt_17MSMT17Person Transfer GAN to Bridge Domain Gap for Person Re-IdentificationPerson Transfer GAN to Bridge Domain Gap for Person Re-Identification[pdf][s2]ec792ad2433b6579f2566c932ee414111e194537
europersonsEuroCity PersonsThe EuroCity Persons Dataset: A Novel Benchmark for Object DetectionThe EuroCity Persons Dataset: A Novel Benchmark for Object Detection[pdf][s2]f0e17f27f029db4ad650ff278fe3c10ecb6cb0c4
pkuPKUSwiss-System Based Cascade Ranking for Gait-based Person Re-identificationSwiss-System Based Cascade Ranking for Gait-Based Person Re-Identification[pdf][s2]f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f
caltech_pedestriansCaltech PedestriansPedestrian Detection: An Evaluation of the State of the ArtPedestrian Detection: An Evaluation of the State of the Art[pdf][s2]f72f6a45ee240cc99296a287ff725aaa7e7ebb35
iit_dehli_earIIT Dehli EarAutomated human identification using ear imagingAutomated Human Identification Using Ear Imaging[pdf][s2]faf40ce28857aedf183e193486f5b4b0a8c478a2
nd_2006ND-2006Using a Multi-Instance Enrollment Representation to Improve 3D Face RecognitionUsing a Multi-Instance Enrollment Representation to Improve 3D Face Recognition[pdf][s2]fd8168f1c50de85bac58a8d328df0a50248b16ae
\ No newline at end of file diff --git a/scraper/reports/paper_title_report_nonmatching.html b/scraper/reports/paper_title_report_nonmatching.html index 7e6ca73c..d24cec59 100644 --- a/scraper/reports/paper_title_report_nonmatching.html +++ b/scraper/reports/paper_title_report_nonmatching.html @@ -1,3 +1,3 @@ -Paper Titles that do not match

Paper Titles that do not match

keynameour titlefound titleaddresss2 id
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]University of California, Irvine0e986f51fe45b00633de9fd0c94d082d2be51406
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
bjut_3dBJUT-3DThe BJUT-3D Large-Scale Chinese Face DatabaseA novel face recognition method based on 3D face model[pdf][s2]1ed1a49534ad8dd00f81939449f6389cfbc25321
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
brainwashBrainwashBrainwash datasetBrainwash: A Data System for Feature Engineering[pdf][s2]214c966d1f9c2a4b66f4535d9a0d4078e63a5867
camelCAMELCAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and TrackingApplication of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis[pdf][s2]5801690199c1917fa58c35c3dead177c0b8f9f2d
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]06f02199690961ba52997cde1527e714d2b3bf8f
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]Jacobs University070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
disfaDISFADISFA: A Spontaneous Facial Action Intensity DatabaseExtended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions[pdf][s2]a5acda0e8c0937bfed013e6382da127103e41395
face_research_labFace Research Lab LondonFace Research Lab London Set. figshareAnxiety promotes memory for mood-congruent faces but does not alter loss aversion.[pdf][s2]University College Londonc6526dd3060d63a6c90e8b7ff340383c4e0e0dd8
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]University of Massachusetts75da1df4ed319926c544eefe17ec8d720feef8c0
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroGeneralização cartográfica automatizada para um banco de dados cadastral[pdf][s2]b6b1b0632eb9d4ab1427278f5e5c46f97753c73d
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
gavab_dbGavabGavabDB: a 3D face databaseExpression invariant 3D face recognition with a Morphable Model[pdf][s2]42505464808dfb446f521fc6ff2cfeffd4d68ff1
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
hi4d_adsipHi4D-ADSIPHi4D-ADSIP 3-D dynamic facial articulation databaseHigh-resolution comprehensive 3-D dynamic database for facial articulation analysis[pdf][s2]24830e3979d4ed01b9fd0feebf4a8fd22e0c35fd
hrt_transgenderHRT TransgenderFace recognition across gender transformation using SVM ClassifierFace recognition: A literature survey[pdf][s2]28312c3a47c1be3a67365700744d3d6665b86f22
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
ilids_mctsImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]21d9d0deed16f0ad62a4865e9acf0686f4f15492
imm_faceIMM Face DatasetThe IMM Face Database - An Annotated Dataset of 240 Face ImagesAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
kdefKDEFThe Karolinska Directed Emotional Faces – KDEFGaze fixation and the neural circuitry of face processing in autism[pdf][s2]93884e46c49f7ae1c7c34046fbc28882f2bd6341
kittiKITTIVision meets Robotics: The KITTI DatasetThe Role of Machine Vision for Intelligent Vehicles[pdf][s2]35ba4ebfd017a56b51e967105af9ae273c9b0178
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]University of Massachusetts2d3482dcff69c7417c7b933f22de606a0e8e42d4
m2vtsm2vtsThe M2VTS Multimodal Face Database (Release 1.00)The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations[pdf][s2]2485c98aa44131d1a2f7d1355b1e372f2bb148ad
m2vtsdb_extendedxm2vtsdbXM2VTSDB: The Extended M2VTS DatabaseLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]Chinese Academy of Sciences45e616093a92e5f1e61a7c6037d5f637aa8964af
mcgillMcGill Real WorldHierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World VideosRobust semi-automatic head pose labeling for real-world face video sequences[pdf][s2]McGill Universityc570d1247e337f91e555c3be0e8c8a5aba539d9f
motMOTEvaluating Multiple Object Tracking Performance: The CLEAR MOT MetricsLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
motMOTPerformance Measures and a Data Set for Multi-Target, Multi-Camera TrackingLearning to associate: HybridBoosted multi-target tracker for crowded scene[pdf][s2]5981e6479c3fd4e31644db35d236bfb84ae46514
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
mtflMTFLFacial Landmark Detection by Deep Multi-task LearningLearning Deep Representation for Face Alignment with Auxiliary Attributes[pdf][s2]a0fd85b3400c7b3e11122f44dc5870ae2de9009a
muctMUCTThe MUCT Landmarked Face DatabaseAnnotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization[pdf][s2]a74251efa970b92925b89eeef50a5e37d9281ad0
multi_pieMULTIPIEMulti-PIEScheduling heterogeneous multi-cores through performance impact estimation (PIE)[pdf][s2]109df0e8e5969ddf01e073143e83599228a1163f
names_and_faces_newsNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]7f4040b482d16354d5938c1d1b926b544652bf5b
pilot_parliamentPPBGender Shades: Intersectional Accuracy Disparities in Commercial Gender Classi cationSummary of Research on Informant Accuracy in Network Data, 11 and on the Reverse Small World Problem[pdf][s2]fb82681ac5d3487bd8e52dbb3d1fa220eeac855e
put_facePut FaceThe PUT face databaseLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
scfaceSCfaceSCface – surveillance cameras face databaseLarge Variability Surveillance Camera Face Database[pdf][s2]f3b84a03985de3890b400b68e2a92c0a00afd9d0
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
sheffieldSheffield FaceFace Recognition: From Theory to ApplicationsFace Description with Local Binary Patterns: Application to Face Recognition[pdf][s2]3607afdb204de9a5a9300ae98aa4635d9effcda2
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
sun_attributesSUNSUN Attribute Database: +Paper Titles that do not match

Paper Titles that do not match

keynameour titlefound titleaddresss2 id
ilids_mctsi-LIDSImagery Library for Intelligent Detection Systems: The i-LIDS User GuideImagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems[pdf][s2]0297448f3ed948e136bb06ceff10eccb34e5bb77
columbia_gazeColumbia GazeGaze Locking: Passive Eye Contact Detection for Human–Object InteractionGaze locking: passive eye contact detection for human-object interaction[pdf][s2]06f02199690961ba52997cde1527e714d2b3bf8f
d3dfacsD3DFACSA FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial ModellingA FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling[pdf][s2]Jacobs University070de852bc6eb275d7ca3a9cdde8f6be8795d1a3
casablancaCasablancaContext-aware {CNNs} for person head detectionContext-Aware CNNs for Person Head Detection[pdf][s2]0ceda9dae8b9f322df65ca2ef02caca9758aec6f
afwAFWFace detection, pose estimation and landmark localization in the wildFace detection, pose estimation, and landmark localization in the wild[pdf][s2]University of California, Irvine0e986f51fe45b00633de9fd0c94d082d2be51406
frgcFRGCOverview of the Face Recognition Grand ChallengeOverview of the face recognition grand challenge[pdf][s2]18ae7c9a4bbc832b8b14bc4122070d7939f5e00e
images_of_groupsImages of GroupsUnderstanding Groups of Images of PeopleUnderstanding images of groups of people[pdf][s2]21d9d0deed16f0ad62a4865e9acf0686f4f15492
lfwLFWLabeled Faces in the Wild: Updates and New Reporting ProceduresLabeled Faces in the Wild : Updates and New Reporting Procedures[pdf][s2]University of Massachusetts2d3482dcff69c7417c7b933f22de606a0e8e42d4
names_and_faces_newsNews DatasetNames and FacesNames and faces in the news[pdf][s2]2fda164863a06a92d3a910b96eef927269aeb730
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
lfwLFWLabeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained EnvironmentsLabeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments[pdf][s2]370b5757a5379b15e30d619e4d3fb9e8e13f3256
georgia_tech_face_databaseGeorgia Tech FaceMaximum likelihood training of the embedded HMM for face detection and recognitionMAXIMUM LIKELIHOOD TRAINING OF THE EMBEDDED HMM FOR FACE DETECTION AND RECOGNITION Ara V. Ne an and Monson H. Hayes III Center for Signal and Image Processing School of Electrical and Computer Engineering[pdf][s2]3dc3f0b64ef80f573e3a5f96e456e52ee980b877
malfMALFFine-grained Evaluation on Face Detection in the Wild.Fine-grained evaluation on face detection in the wild[pdf][s2]Chinese Academy of Sciences45e616093a92e5f1e61a7c6037d5f637aa8964af
am_fedAM-FEDAffectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"[pdf][s2]47aeb3b82f54b5ae8142b4bdda7b614433e69b9a
cmu_pieCMU PIEThe CMU Pose, Illumination, and Expression DatabaseThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
multi_pieMULTIPIEMulti-PIEThe CMU Pose, Illumination, and Expression (PIE) Database[pdf][s2]4d423acc78273b75134e2afd1777ba6d3a398973
3dddb_unconstrained3D DynamicA 3D Dynamic Database for Unconstrained Face RecognitionA 3 D Dynamic Database for Unconstrained Face Recognition[pdf][s2]4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461
cfdCFDThe Chicago face database: A free stimulus set of faces and norming dataThe Chicago face database: A free stimulus set of faces and norming data.[pdf][s2]4df3143922bcdf7db78eb91e6b5359d6ada004d2
dartmouth_childrenDartmouth ChildrenThe Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus setThe Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set[pdf][s2]4e6ee936eb50dd032f7138702fa39b7c18ee8907
ifadIFADIndian Face Age Database: A Database for Face Recognition with Age VariationIndian Face Age Database : A Database for Face Recognition with Age Variation[pdf][s2]55c40cbcf49a0225e72d911d762c27bb1c2d14aa
stanford_droneStanford DroneLearning Social Etiquette: Human Trajectory Prediction In Crowded ScenesSocial LSTM: Human Trajectory Prediction in Crowded Spaces[pdf][s2]570f37ed63142312e6ccdf00ecc376341ec72b9f
ijb_cIJB-CIARPA Janus Benchmark CIARPA Janus Benchmark - C: Face Dataset and Protocol[pdf][s2]57178b36c21fd7f4529ac6748614bb3374714e91
mr2MR2The MR2: A multi-racial mega-resolution database of facial stimuliThe MR2: A multi-racial, mega-resolution database of facial stimuli.[pdf][s2]578d4ad74818086bb64f182f72e2c8bd31e3d426
cvc_01_barcelonaCVC-01Adaptive Image Sampling and Windows Classification for On-board Pedestrian DetectionAdaptive Image Sampling and Windows Classification for On – board Pedestrian Detection[pdf][s2]57fe081950f21ca03b5b375ae3e84b399c015861
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
alert_airportALERT AirportA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and DatasetsA Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.[pdf][s2]6403117f9c005ae81f1e8e6d1302f4a045e3d99d
afadAFADOrdinal Regression with a Multiple Output CNN for Age EstimationOrdinal Regression with Multiple Output CNN for Age Estimation[pdf][s2]6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c
stickmen_buffyBuffy StickmenLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALClustered Pose and Nonlinear Appearance Models for Human Pose EstimationLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
stickmen_pascalStickmen PASCALLearning to Parse Images of Articulated ObjectsLearning to parse images of articulated bodies[pdf][s2]6dd0597f8513dc100cd0bc1b493768cde45098a9
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
fddbFDDBFDDB: A Benchmark for Face Detection in Unconstrained SettingsA Benchmark for Face Detection in Unconstrained Settings[pdf][s2]University of Massachusetts75da1df4ed319926c544eefe17ec8d720feef8c0
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
lfwLFWLabeled Faces in the Wild: A SurveyLabeled Faces in the Wild : A Survey[pdf][s2]7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22
nova_emotionsNovaemötions DatasetCompetitive affective gamming: Winning with a smileCompetitive affective gaming: winning with a smile[pdf][s2]7f4040b482d16354d5938c1d1b926b544652bf5b
sun_attributesSUNSUN Attribute Database: Discovering, Annotating, and Recognizing Scene AttributesSUN attribute database: Discovering, annotating, and recognizing scene attributes[pdf][s2]833fa04463d90aab4a9fe2870d480f0b40df446e
tiny_imagesTiny Images80 million tiny images: a large dataset for non-parametric object and scene recognition80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition[pdf][s2]31b58ced31f22eab10bd3ee2d9174e7c14c27c01
umd_facesUMDThe Do's and Don'ts for CNN-based Face VerificationThe Do’s and Don’ts for CNN-Based Face Verification[pdf][s2]71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6
viperVIPeREvaluating Appearance Models for Recognition, Reacquisition, and TrackingEvaluating Appearance Models for Recognition , Reacquisition , and Tracking[pdf][s2]6273b3491e94ea4dd1ce42b791d77bdc96ee73a8
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
wildtrackWildTrackWILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian DetectionWILDTRACK : A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection[pdf][s2]77c81c13a110a341c140995bedb98101b9e84f7f
\ No newline at end of file +discovering, annotating, and recognizing scene attributes&sort=relevance" target="_blank">[s2]		833fa04463d90aab4a9fe2870d480f0b40df446e
10k_US_adult_faces10K US Adult FacesThe intrinsic memorability of face imagesThe intrinsic memorability of face photographs.[pdf][s2]8b2dd5c61b23ead5ae5508bb8ce808b5ea266730
feiFEICaptura e Alinhamento de Imagens: Um Banco de Faces BrasileiroA new ranking method for principal components analysis and its application to face image analysis[pdf][s2]8b56e33f33e582f3e473dba573a16b598ed9bcdc
ilids_vid_reidiLIDS-VIDPerson Re-Identi cation by Video RankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
sdu_vidSDU-VIDPerson reidentification by video rankingPerson Re-identification by Video Ranking[pdf][s2]98bb029afe2a1239c3fdab517323066f0957b81b
who_goes_thereWGTWho Goes There? Approaches to Mapping Facial Appearance DiversityWho goes there?: approaches to mapping facial appearance diversity[pdf][s2]9b9bf5e623cb8af7407d2d2d857bc3f1b531c182
bp4d_spontanousBP4D-SpontanousA high resolution spontaneous 3D dynamic facial expression databaseA high-resolution spontaneous 3D dynamic facial expression database[pdf][s2]SUNY Binghamtonb91f54e1581fbbf60392364323d00a0cd43e493c
hda_plusHDA+A Multi-camera video data set for research on High-Definition surveillanceHDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance[pdf][s2]bd88bb2e4f351352d88ee7375af834360e223498
\ No newline at end of file diff --git a/scraper/s2-final-report.py b/scraper/s2-final-report.py index 3cceff43..675709ea 100644 --- a/scraper/s2-final-report.py +++ b/scraper/s2-final-report.py @@ -183,6 +183,8 @@ def load_megapixels_lookup(): rec = {} for index, key in enumerate(keys): rec[key] = row[index] + if rec['paper_id'] == "": + continue paper_key = rec['key'] if paper_key not in lookup: rec['paper_ids'] = [] diff --git a/scraper/s2-geocode-server.py b/scraper/s2-geocode-server.py index 021921fe..1c624a52 100644 --- a/scraper/s2-geocode-server.py +++ b/scraper/s2-geocode-server.py @@ -52,3 +52,7 @@ def list_citations(citation): def geocode_paper(): return jsonify({ }) + +if __name__=="__main__": + app.run("0.0.0.0", debug=False) + diff --git a/scraper/s2-papers.py b/scraper/s2-papers.py index 58aebcc6..744454b7 100644 --- a/scraper/s2-papers.py +++ b/scraper/s2-papers.py @@ -28,7 +28,11 @@ def fetch_papers(): name = line[1] title = line[2] paper_id = line[3] + if paper_id == '': + continue paper = fetch_paper(s2, paper_id) + if paper is None: + continue db_paper = load_paper(paper_id) pdf_link = db_paper.pdf_link if db_paper else "" diff --git a/scraper/util.py b/scraper/util.py index 2d7c2ccb..6f3fc08b 100644 --- a/scraper/util.py +++ b/scraper/util.py @@ -331,11 +331,11 @@ def fetch_paper(s2, paper_id): print(paper_id) paper = s2.paper(paper_id) if paper is None: - print("Got none paper??") + print("Paper not found: {}".format(paper_id)) # time.sleep(random.randint(1, 2)) paper = s2.paper(paper_id) if paper is None: - print("Paper not found") + # print("Paper not found") return None write_json(paper_fn, paper) # time.sleep(random.randint(1, 2)) -- cgit v1.2.3-70-g09d2