| key | name | our title | found title | | | address | s2 id | | 10k_US_adult_faces | 10K US Adult Faces | The intrinsic memorability of face images | The intrinsic memorability of face photographs. | [pdf] | [s2] | | 8b2dd5c61b23ead5ae5508bb8ce808b5ea266730 |
| 3d_rma | 3D-RMA | Automatic 3D Face Authentication | Automatic 3D face authentication | [pdf] | [s2] | | 2160788824c4c29ffe213b2cbeb3f52972d73f37 |
| 3dddb_unconstrained | 3D Dynamic | A 3D Dynamic Database for Unconstrained Face Recognition | A 3D Dynamic Database for Unconstrained Face Recognition | [pdf] | [s2] | | 4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461 |
| 3dpes | 3DPeS | 3DPes: 3D People Dataset for Surveillance and Forensics | 3DPeS: 3D people dataset for surveillance and forensics | [pdf] | [s2] | | 2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e |
| 4dfab | 4DFAB | 4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications | 4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications | [pdf] | [s2] | | a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43 |
| fpoq | 50 People One Question | Merging Pose Estimates Across Space and Time | Merging Pose Estimates Across Space and Time | [pdf] | [s2] | | 5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725 |
| adience | Adience | Age and Gender Estimation of Unfiltered Faces | Age and Gender Estimation of Unfiltered Faces | [pdf] | [s2] | | 1be498d4bbc30c3bfd0029114c784bc2114d67c0 |
| afad | AFAD | Ordinal Regression with a Multiple Output CNN for Age Estimation | Ordinal Regression with Multiple Output CNN for Age Estimation | [pdf] | [s2] | | 6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c |
| afew_va | AFEW-VA | AFEW-VA database for valence and arousal estimation in-the-wild | AFEW-VA database for valence and arousal estimation in-the-wild | [pdf] | [s2] | | 2624d84503bc2f8e190e061c5480b6aa4d89277a |
| affectnet | AffectNet | AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild | AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild | [pdf] | [s2] | | 758d7e1be64cc668c59ef33ba8882c8597406e53 |
| aflw | AFLW | Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization | Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization | [pdf] | [s2] | | a74251efa970b92925b89eeef50a5e37d9281ad0 |
| afw | AFW | Face detection, pose estimation and landmark localization in the wild | Face detection, pose estimation, and landmark localization in the wild | [pdf] | [s2] | | 0e986f51fe45b00633de9fd0c94d082d2be51406 |
| agedb | AgeDB | AgeDB: the first manually collected, in-the-wild age database | AgeDB: The First Manually Collected, In-the-Wild Age Database | [pdf] | [s2] | | d818568838433a6d6831adde49a58cef05e0c89f |
| alert_airport | ALERT Airport | A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets | A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets | [pdf] | [s2] | | 6403117f9c005ae81f1e8e6d1302f4a045e3d99d |
| am_fed | AM-FED | Affectiva 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 |
| apis | APiS1.0 | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | [pdf] | [s2] | | 488e475eeb3bb39a145f23ede197cd3620f1d98a |
| appa_real | APPA-REAL | Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database | Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database | [pdf] | [s2] | | 633c851ebf625ad7abdda2324e9de093cf623141 |
| appa_real | APPA-REAL | From Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation | From Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation | [pdf] | [s2] | | 7b92d1e53cc87f7a4256695de590098a2f30261e |
| ar_facedb | AR Face | The AR Face Database | The AR face database | [pdf] | [s2] | | 6d96f946aaabc734af7fe3fc4454cf8547fcd5ed |
| awe_ears | AWE Ears | Ear Recognition: More Than a Survey | Ear Recognition: More Than a Survey | [pdf] | [s2] | | 84fe5b4ac805af63206012d29523a1e033bc827e |
| b3d_ac | B3D(AC) | A 3-D Audio-Visual Corpus of Affective Communication | A 3-D Audio-Visual Corpus of Affective Communication | [pdf] | [s2] | | d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae |
| bbc_pose | BBC Pose | Automatic and Efficient Human Pose Estimation for Sign Language Videos | Automatic and Efficient Human Pose Estimation for Sign Language Videos | [pdf] | [s2] | | 213a579af9e4f57f071b884aa872651372b661fd |
| bfm | BFM | A 3D Face Model for Pose and Illumination Invariant Face Recognition | A 3D Face Model for Pose and Illumination Invariant Face Recognition | [pdf] | [s2] | | 639937b3a1b8bded3f7e9a40e85bd3770016cf3c |
| bio_id | BioID Face | Robust Face Detection Using the Hausdorff Distance | Robust Face Detection Using the Hausdorff Distance | [pdf] | [s2] | | 4053e3423fb70ad9140ca89351df49675197196a |
| bosphorus | The Bosphorus | Bosphorus Database for 3D Face Analysis | Bosphorus Database for 3D Face Analysis | [pdf] | [s2] | | 2acf7e58f0a526b957be2099c10aab693f795973 |
| bp4d_plus | BP4D+ | Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis | Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis | [pdf] | [s2] | | 53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4 |
| bpad | BPAD | Describing People: A Poselet-Based Approach to Attribute Classification | Describing people: A poselet-based approach to attribute classification | [pdf] | [s2] | | 7808937b46acad36e43c30ae4e9f3fd57462853d |
| brainwash | Brainwash | End-to-End People Detection in Crowded Scenes | End-to-End People Detection in Crowded Scenes | [pdf] | [s2] | | 1bd1645a629f1b612960ab9bba276afd4cf7c666 |
| bu_3dfe | BU-3DFE | A 3D Facial Expression Database For Facial Behavior Research | A 3D facial expression database for facial behavior research | [pdf] | [s2] | | cc589c499dcf323fe4a143bbef0074c3e31f9b60 |
| cacd | | Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval | Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval | [pdf] | [s2] | | c44c84540db1c38ace232ef34b03bda1c81ba039 |
| cafe | #N/A | The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults | The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults | [pdf] | [s2] | | 20388099cc415c772926e47bcbbe554e133343d1 |
| caltech_10k_web_faces | Caltech 10K Web Faces | Pruning Training Sets for Learning of Object Categories | Pruning training sets for learning of object categories | [pdf] | [s2] | | 636b8ffc09b1b23ff714ac8350bb35635e49fa3c |
| caltech_crp | Caltech CRP | Fine-grained classification of pedestrians in video: Benchmark and state of the art | Fine-grained classification of pedestrians in video: Benchmark and state of the art | [pdf] | [s2] | | 060820f110a72cbf02c14a6d1085bd6e1d994f6a |
| caltech_pedestrians | Caltech Pedestrians | Pedestrian Detection: A Benchmark | Pedestrian detection: A benchmark | [pdf] | [s2] | | 1dc35905a1deff8bc74688f2d7e2f48fd2273275 |
| cas_peal | CAS-PEAL | The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations | The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations | [pdf] | [s2] | | 2485c98aa44131d1a2f7d1355b1e372f2bb148ad |
| casablanca | Casablanca | Context-aware {CNNs} for person head detection | Context-Aware CNNs for Person Head Detection | [pdf] | [s2] | | 0ceda9dae8b9f322df65ca2ef02caca9758aec6f |
| casia_webface | CASIA Webface | Learning Face Representation from Scratch | Learning Face Representation from Scratch | [pdf] | [s2] | | 853bd61bc48a431b9b1c7cab10c603830c488e39 |
| celeba | CelebA | Deep Learning Face Attributes in the Wild | Deep Learning Face Attributes in the Wild | [pdf] | [s2] | | 6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4 |
| cfd | CFD | The Chicago face database: A free stimulus set of faces and norming data | The Chicago face database: A free stimulus set of faces and norming data. | [pdf] | [s2] | | 4df3143922bcdf7db78eb91e6b5359d6ada004d2 |
| chalearn | ChaLearn | ChaLearn Looking at People: A Review of Events and Resources | ChaLearn looking at people: A review of events and resources | [pdf] | [s2] | | 8d5998cd984e7cce307da7d46f155f9db99c6590 |
| chokepoint | ChokePoint | Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition | Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition | [pdf] | [s2] | | 0486214fb58ee9a04edfe7d6a74c6d0f661a7668 |
| clothing_co_parsing | CCP | Clothing Co-Parsing by Joint Image Segmentation and Labeling | Clothing Co-parsing by Joint Image Segmentation and Labeling | [pdf] | [s2] | | 2bf8541199728262f78d4dced6fb91479b39b738 |
| cmdp | CMDP | Distance Estimation of an Unknown Person from a Portrait | Distance Estimation of an Unknown Person from a Portrait | [pdf] | [s2] | | 56ae6d94fc6097ec4ca861f0daa87941d1c10b70 |
| cmu_pie | CMU PIE | The CMU Pose, Illumination, and Expression Database | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | [s2] | | 4d423acc78273b75134e2afd1777ba6d3a398973 |
| coco | COCO | Microsoft COCO: Common Objects in Context | Microsoft COCO: Common Objects in Context | [pdf] | [s2] | | 5e0f8c355a37a5a89351c02f174e7a5ddcb98683 |
| coco_action | COCO-a | Describing Common Human Visual Actions in Images | Describing Common Human Visual Actions in Images | [pdf] | [s2] | | 4946ba10a4d5a7d0a38372f23e6622bd347ae273 |
| coco_qa | COCO QA | Exploring Models and Data for Image Question Answering | Exploring Models and Data for Image Question Answering | [pdf] | [s2] | | 35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62 |
| cofw | COFW | Robust face landmark estimation under occlusion | Robust Face Landmark Estimation under Occlusion | [pdf] | [s2] | | 2724ba85ec4a66de18da33925e537f3902f21249 |
| cohn_kanade | CK | Comprehensive Database for Facial Expression Analysis | Comprehensive Database for Facial Expression Analysis | [pdf] | [s2] | | 23fc83c8cfff14a16df7ca497661264fc54ed746 |
| complex_activities | Ongoing Complex Activities | Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space | Recognition of ongoing complex activities by sequence prediction over a hierarchical label space | [pdf] | [s2] | | 65355cbb581a219bd7461d48b3afd115263ea760 |
| cuhk_campus_03 | CUHK03 Campus | Human Reidentification with Transferred Metric Learning | Human Reidentification with Transferred Metric Learning | [pdf] | [s2] | | 44484d2866f222bbb9b6b0870890f9eea1ffb2d0 |
| cuhk_campus_03 | CUHK03 Campus | Locally Aligned Feature Transforms across Views | Locally Aligned Feature Transforms across Views | [pdf] | [s2] | | 38b55d95189c5e69cf4ab45098a48fba407609b4 |
| cuhk_campus_03 | CUHK03 Campus | DeepReID: Deep Filter Pairing Neural Network for Person Re-identification | DeepReID: Deep Filter Pairing Neural Network for Person Re-identification | [pdf] | [s2] | | 6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3 |
| cvc_01_barcelona | CVC-01 | Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection | Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection | [pdf] | [s2] | | 57fe081950f21ca03b5b375ae3e84b399c015861 |
| ufi | UFI | Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions | Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions | [pdf] | [s2] | | 4b4106614c1d553365bad75d7866bff0de6056ed |
| d3dfacs | D3DFACS | A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling | A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling | [pdf] | [s2] | | 070de852bc6eb275d7ca3a9cdde8f6be8795d1a3 |
| dartmouth_children | Dartmouth Children | The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set | The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set | [pdf] | [s2] | | 4e6ee936eb50dd032f7138702fa39b7c18ee8907 |
| data_61 | Data61 Pedestrian | A Multi-Modal Graphical Model for Scene Analysis | A Multi-modal Graphical Model for Scene Analysis | [pdf] | [s2] | | 563c940054e4b456661762c1ab858e6f730c3159 |
| deep_fashion | DeepFashion | DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations | DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations | [pdf] | [s2] | | 18010284894ed0edcca74e5bf768ee2e15ef7841 |
| deep_fashion | DeepFashion | Fashion Landmark Detection in the Wild | Fashion Landmark Detection in the Wild | [pdf] | [s2] | | 4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7 |
| distance_nighttime | Long Distance Heterogeneous Face | Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching | Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching | [pdf] | [s2] | | 4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06 |
| duke_mtmc | Duke MTMC | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking | [pdf] | [s2] | | 27a2fad58dd8727e280f97036e0d2bc55ef5424c |
| duke_mtmc | Duke MTMC | Improving Person Re-identification by Attribute and Identity Learning | Improving Person Re-identification by Attribute and Identity Learning | [pdf] | [s2] | | 7f23a4bb0c777dd72cca7665a5f370ac7980217e |
| duke_mtmc | Duke MTMC | Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro | Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro | [pdf] | [s2] | | 15e1af79939dbf90790b03d8aa02477783fb1d0f |
| duke_mtmc | Duke MTMC | Tracking Multiple People Online and in Real Time | Tracking Multiple People Online and in Real Time | [pdf] | [s2] | | 64e0690dd176a93de9d4328f6e31fc4afe1e7536 |
| emotio_net | EmotioNet Database | EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild | EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild | [pdf] | [s2] | | c900e0ad4c95948baaf0acd8449fde26f9b4952a |
| erce | ERCe | Video Synopsis by Heterogeneous Multi-source Correlation | Video Synopsis by Heterogeneous Multi-source Correlation | [pdf] | [s2] | | b6c293f0420f7e945b5916ae44269fb53e139275 |
| erce | ERCe | Learning from Multiple Sources for Video Summarisation | Learning from Multiple Sources for Video Summarisation | [pdf] | [s2] | | 287ddcb3db5562235d83aee318f318b8d5e43fb1 |
| europersons | EuroCity Persons | The EuroCity Persons Dataset: A Novel Benchmark for Object Detection | The EuroCity Persons Dataset: A Novel Benchmark for Object Detection | [pdf] | [s2] | | 72a155c987816ae81c858fddbd6beab656d86220 |
| expw | ExpW | From Facial Expression Recognition to Interpersonal Relation Prediction | From Facial Expression Recognition to Interpersonal Relation Prediction | [pdf] | [s2] | | 22f656d0f8426c84a33a267977f511f127bfd7f3 |
| face_scrub | FaceScrub | A data-driven approach to cleaning large face datasets | A data-driven approach to cleaning large face datasets | [pdf] | [s2] | | 0d3bb75852098b25d90f31d2f48fd0cb4944702b |
| face_tracer | FaceTracer | FaceTracer: A Search Engine for Large Collections of Images with Faces | FaceTracer: A Search Engine for Large Collections of Images with Faces | [pdf] | [s2] | | 4c170a0dcc8de75587dae21ca508dab2f9343974 |
| face_tracer | FaceTracer | Face Swapping: Automatically Replacing Faces in Photographs | Face swapping: automatically replacing faces in photographs | [pdf] | [s2] | | 670637d0303a863c1548d5b19f705860a23e285c |
| faceplace | Face Place | Recognizing disguised faces | Recognizing disguised faces | [pdf] | [s2] | | 25474c21613607f6bb7687a281d5f9d4ffa1f9f3 |
| fddb | FDDB | FDDB: A Benchmark for Face Detection in Unconstrained Settings | FDDB: A benchmark for face detection in unconstrained settings | [pdf] | [s2] | | 75da1df4ed319926c544eefe17ec8d720feef8c0 |
| fei | FEI | Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro | A new ranking method for principal components analysis and its application to face image analysis | [pdf] | [s2] | | 8b56e33f33e582f3e473dba573a16b598ed9bcdc |
| feret | FERET | The FERET Verification Testing Protocol for Face Recognition Algorithms | The FERET Verification Testing Protocol for Face Recognition Algorithms | [pdf] | [s2] | | 0c4a139bb87c6743c7905b29a3cfec27a5130652 |
| feret | FERET | The FERET Evaluation Methodology for Face-Recognition Algorithms | The FERET Evaluation Methodology for Face-Recognition Algorithms | [pdf] | [s2] | | 0f0fcf041559703998abf310e56f8a2f90ee6f21 |
| feret | FERET | FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results | FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results | [pdf] | [s2] | | 31de9b3dd6106ce6eec9a35991b2b9083395fd0b |
| feret | FERET | The FERET database and evaluation procedure for face-recognition algorithms | The FERET database and evaluation procedure for face-recognition algorithms | [pdf] | [s2] | | dc8b25e35a3acb812beb499844734081722319b4 |
| ferplus | FER+ | Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution | Training deep networks for facial expression recognition with crowd-sourced label distribution | [pdf] | [s2] | | 298cbc3dfbbb3a20af4eed97906650a4ea1c29e0 |
| fia | CMU FiA | The CMU Face In Action (FIA) Database | The CMU Face In Action (FIA) Database | [pdf] | [s2] | | 47662d1a368daf70ba70ef2d59eb6209f98b675d |
| fiw_300 | 300-W | A semi-automatic methodology for facial landmark annotation | A Semi-automatic Methodology for Facial Landmark Annotation | [pdf] | [s2] | | 013909077ad843eb6df7a3e8e290cfd5575999d2 |
| fiw_300 | 300-W | 300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge | 300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge | [pdf] | [s2] | | 044d9a8c61383312cdafbcc44b9d00d650b21c70 |
| fiw_300 | 300-W | 300 faces In-the-wild challenge: Database and results | 300 Faces In-The-Wild Challenge: database and results | [pdf] | [s2] | | e4754afaa15b1b53e70743880484b8d0736990ff |
| geofaces | GeoFaces | FACE2GPS: Estimating geographic location from facial features | Exploring the geo-dependence of human face appearance | [pdf] | [s2] | | 2cd7821fcf5fae53a185624f7eeda007434ae037 |
| geofaces | GeoFaces | Large-scale geo-facial image analysis | Large-scale geo-facial image analysis | [pdf] | [s2] | | 4af89578ac237278be310f7660a408b03f12d603 |
| geofaces | GeoFaces | Exploring the Geo-Dependence of Human Face Appearance | Exploring the geo-dependence of human face appearance | [pdf] | [s2] | | 2cd7821fcf5fae53a185624f7eeda007434ae037 |
| geofaces | GeoFaces | GeoFaceExplorer: Exploring the Geo-Dependence of Facial Attributes | GeoFaceExplorer: exploring the geo-dependence of facial attributes | [pdf] | [s2] | | 17b46e2dad927836c689d6787ddb3387c6159ece |
| georgia_tech_face_database | Georgia Tech Face | Maximum likelihood training of the embedded HMM for face detection and recognition | Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition | [pdf] | [s2] | | 3dc3f0b64ef80f573e3a5f96e456e52ee980b877 |
| gfw | Grouping Face in the Wild | Merge or Not? Learning to Group Faces via Imitation Learning | Merge or Not? Learning to Group Faces via Imitation Learning | [pdf] | [s2] | | e58dd160a76349d46f881bd6ddbc2921f08d1050 |
| graz | Graz Pedestrian | Weak Hypotheses and Boosting for Generic Object Detection and Recognition | Weak Hypotheses and Boosting for Generic Object Detection and Recognition | [pdf] | [s2] | | 0c91808994a250d7be332400a534a9291ca3b60e |
| h3d | H3D | Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations | Poselets: Body part detectors trained using 3D human pose annotations | [pdf] | [s2] | | 2830fb5282de23d7784b4b4bc37065d27839a412 |
| hda_plus | HDA+ | The HDA+ data set for research on fully automated re-identification systems | The HDA+ Data Set for Research on Fully Automated Re-identification Systems | [pdf] | [s2] | | 8f02ec0be21461fbcedf51d864f944cfc42c875f |
| hda_plus | HDA+ | A Multi-camera video data set for research on High-Definition surveillance | HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance | [pdf] | [s2] | | bd88bb2e4f351352d88ee7375af834360e223498 |
| helen | Helen | Interactive Facial Feature Localization | Interactive Facial Feature Localization | [pdf] | [s2] | | 95f12d27c3b4914e0668a268360948bce92f7db3 |
| hi4d_adsip | Hi4D-ADSIP | Hi4D-ADSIP 3-D dynamic facial articulation database | Hi4D-ADSIP 3-D dynamic facial articulation database | [pdf] | [s2] | | a8d0b149c2eadaa02204d3e4356fbc8eccf3b315 |
| hipsterwars | Hipsterwars | Hipster Wars: Discovering Elements of Fashion Styles | Hipster Wars: Discovering Elements of Fashion Styles | [pdf] | [s2] | | 04c2cda00e5536f4b1508cbd80041e9552880e67 |
| hollywood_headset | HollywoodHeads | Context-aware CNNs for person head detection | Context-Aware CNNs for Person Head Detection | [pdf] | [s2] | | 0ceda9dae8b9f322df65ca2ef02caca9758aec6f |
| hrt_transgender | HRT Transgender | Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset | Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset | [pdf] | [s2] | | 137aa2f891d474fce1e7a1d1e9b3aefe21e22b34 |
| ibm_dif | IBM Diversity in Faces | Diversity in Faces | Facial Coding Scheme Reference 1 Craniofacial Distances | [pdf] | [s2] | | 0ab7cff2ccda7269b73ff6efd9d37e1318f7db25 |
| ifad | IFAD | Indian Face Age Database: A Database for Face Recognition with Age Variation | Indian Face Age Database: A Database for Face Recognition with Age Variation | [pdf] | [s2] | | 55c40cbcf49a0225e72d911d762c27bb1c2d14aa |
| ifdb | IFDB | Iranian Face Database and Evaluation with a New Detection Algorithm | Iranian Face Database and Evaluation with a New Detection Algorithm | [pdf] | [s2] | | 066d71fcd997033dce4ca58df924397dfe0b5fd1 |
| iit_dehli_ear | IIT Dehli Ear | Automated human identification using ear imaging | Automated Human Identification Using Ear Imaging | [pdf] | [s2] | | faf40ce28857aedf183e193486f5b4b0a8c478a2 |
| ijb_b | IJB-B | IARPA Janus Benchmark-B Face Dataset | IARPA Janus Benchmark-B Face Dataset | [pdf] | [s2] | | 0cb2dd5f178e3a297a0c33068961018659d0f443 |
| ijb_a | IJB-A | Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A | Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A | [pdf] | [s2] | | 140c95e53c619eac594d70f6369f518adfea12ef |
| ijb_c | IJB-C | IARPA Janus Benchmark C | IARPA Janus Benchmark - C: Face Dataset and Protocol | [pdf] | [s2] | | 57178b36c21fd7f4529ac6748614bb3374714e91 |
| ilids_mcts | i-LIDS Multiple-Camera | Imagery Library for Intelligent Detection Systems: The i-LIDS User Guide | Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems | [pdf] | [s2] | | 0297448f3ed948e136bb06ceff10eccb34e5bb77 |
| ilids_mcts_vid | iLIDS-VID | Person Re-Identi cation by Video Ranking | Person Re-identification by Video Ranking | [pdf] | [s2] | | 98bb029afe2a1239c3fdab517323066f0957b81b |
| imdb_face | IMDb Face | The Devil of Face Recognition is in the Noise | The Devil of Face Recognition is in the Noise | [pdf] | [s2] | | 9e31e77f9543ab42474ba4e9330676e18c242e72 |
| imdb_wiki | IMDB-Wiki | Deep expectation of real and apparent age from a single image without facial landmarks | Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks | [pdf] | [s2] | | 10195a163ab6348eef37213a46f60a3d87f289c5 |
| imdb_wiki | IMDB-Wiki | DEX: Deep EXpectation of apparent age from a single image | DEX: Deep EXpectation of Apparent Age from a Single Image | [pdf] | [s2] | | 8355d095d3534ef511a9af68a3b2893339e3f96b |
| immediacy | Immediacy | Multi-task Recurrent Neural Network for Immediacy Prediction | Multi-task Recurrent Neural Network for Immediacy Prediction | [pdf] | [s2] | | 1e3df3ca8feab0b36fd293fe689f93bb2aaac591 |
| imsitu | imSitu | Situation Recognition: Visual Semantic Role Labeling for Image Understanding | Situation Recognition: Visual Semantic Role Labeling for Image Understanding | [pdf] | [s2] | | 51eba481dac6b229a7490f650dff7b17ce05df73 |
| jaffe | JAFFE | Coding Facial Expressions with Gabor Wavelets | Coding Facial Expressions with Gabor Wavelets | [pdf] | [s2] | | 45c31cde87258414f33412b3b12fc5bec7cb3ba9 |
| jpl_pose | JPL-Interaction dataset | First-Person Activity Recognition: What Are They Doing to Me? | First-Person Activity Recognition: What Are They Doing to Me? | [pdf] | [s2] | | 1aad2da473888cb7ebc1bfaa15bfa0f1502ce005 |
| kin_face | UB KinFace | Understanding Kin Relationships in a Photo | Understanding Kin Relationships in a Photo | [pdf] | [s2] | | 08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7 |
| kin_face | UB KinFace | Kinship Verification through Transfer Learning | Kinship Verification through Transfer Learning | [pdf] | [s2] | | 4793f11fbca4a7dba898b9fff68f70d868e2497c |
| kitti | KITTI | Vision meets Robotics: The KITTI Dataset | Vision meets robotics: The KITTI dataset | [pdf] | [s2] | | 026e3363b7f76b51cc711886597a44d5f1fd1de2 |
| lag | LAG | Large Age-Gap Face Verification by Feature Injection in Deep Networks | Large age-gap face verification by feature injection in deep networks | [pdf] | [s2] | | 0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e |
| laofiw | LAOFIW | Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings | Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings | [pdf] | [s2] | | 4eab317b5ac436a949849ed286baa3de2a541eef |
| large_scale_person_search | Large Scale Person Search | End-to-End Deep Learning for Person Search | End-to-End Deep Learning for Person Search | [pdf] | [s2] | | 2161f6b7ee3c0acc81603b01dc0df689683577b9 |
| leeds_sports_pose | Leeds Sports Pose | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | [pdf] | [s2] | | 4b1d23d17476fcf78f4cbadf69fb130b1aa627c0 |
| lfpw | LFPW | Localizing Parts of Faces Using a Consensus of Exemplars | Localizing Parts of Faces Using a Consensus of Exemplars | [pdf] | [s2] | | 140438a77a771a8fb656b39a78ff488066eb6b50 |
| lfw | LFW | Labeled Faces in the Wild: Updates and New Reporting Procedures | Labeled Faces in the Wild : Updates and New Reporting Procedures | [pdf] | [s2] | | 2d3482dcff69c7417c7b933f22de606a0e8e42d4 |
| lfw | LFW | Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments | Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments | [pdf] | [s2] | | 370b5757a5379b15e30d619e4d3fb9e8e13f3256 |
| lfw | LFW | Labeled Faces in the Wild: A Survey | Labeled Faces in the Wild: A Survey | [pdf] | [s2] | | 7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22 |
| lfw | LFW | Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics | Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics | [pdf] | [s2] | | 133f01aec1534604d184d56de866a4bd531dac87 |
| m2vtsdb_extended | xm2vtsdb | XM2VTSDB: The Extended M2VTS Database | XM2VTSDB : The extended M2VTS database | [pdf] | [s2] | | b62628ac06bbac998a3ab825324a41a11bc3a988 |
| mafa | MAsked FAces | Detecting Masked Faces in the Wild with LLE-CNNs | Detecting Masked Faces in the Wild with LLE-CNNs | [pdf] | [s2] | | 9cc8cf0c7d7fa7607659921b6ff657e17e135ecc |
| mafl | MAFL | Facial Landmark Detection by Deep Multi-task Learning | Facial Landmark Detection by Deep Multi-task Learning | [pdf] | [s2] | | 8a3c5507237957d013a0fe0f082cab7f757af6ee |
| mafl | MAFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | [s2] | | a0fd85b3400c7b3e11122f44dc5870ae2de9009a |
| malf | MALF | Fine-grained Evaluation on Face Detection in the Wild. | Fine-grained evaluation on face detection in the wild | [pdf] | [s2] | | 45e616093a92e5f1e61a7c6037d5f637aa8964af |
| mapillary | Mapillary | The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes | The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes | [pdf] | [s2] | | 79828e6e9f137a583082b8b5a9dfce0c301989b8 |
| market_1501 | Market 1501 | Improving Person Re-identification by Attribute and Identity Learning | Improving Person Re-identification by Attribute and Identity Learning | [pdf] | [s2] | | 7f23a4bb0c777dd72cca7665a5f370ac7980217e |
| market_1501 | Market 1501 | Scalable Person Re-identification: A Benchmark | Scalable Person Re-identification: A Benchmark | [pdf] | [s2] | | 4308bd8c28e37e2ed9a3fcfe74d5436cce34b410 |
| market_1501 | Market 1501 | Orientation Driven Bag of Appearances for Person Re-identification | Orientation Driven Bag of Appearances for Person Re-identification | [pdf] | [s2] | | a7fe834a0af614ce6b50dc093132b031dd9a856b |
| mars | MARS | MARS: A Video Benchmark for Large-Scale Person Re-identification | MARS: A Video Benchmark for Large-Scale Person Re-Identification | [pdf] | [s2] | | c0387e788a52f10bf35d4d50659cfa515d89fbec |
| mcgill | McGill Real World | Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos | Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos | [pdf] | [s2] | | 2d45cfd838016a6e39f6b766ffe85acd649440c7 |
| megaage | MegaAge | Quantifying Facial Age by Posterior of Age Comparisons | Quantifying Facial Age by Posterior of Age Comparisons | [pdf] | [s2] | | c72a2ea819df9b0e8cd267eebcc6528b8741e03d |
| megaface | MegaFace | Level Playing Field for Million Scale Face Recognition | Level Playing Field for Million Scale Face Recognition | [pdf] | [s2] | | 28d4e027c7e90b51b7d8908fce68128d1964668a |
| megaface | MegaFace | The MegaFace Benchmark: 1 Million Faces for Recognition at Scale | The MegaFace Benchmark: 1 Million Faces for Recognition at Scale | [pdf] | [s2] | | 96e0cfcd81cdeb8282e29ef9ec9962b125f379b0 |
| mit_cbcl | MIT CBCL | Component-based Face Recognition with 3D Morphable Models | Component-Based Face Recognition with 3D Morphable Models | [pdf] | [s2] | | 079a0a3bf5200994e1f972b1b9197bf2f90e87d4 |
| mmi_facial_expression | MMI Facial Expression Dataset | WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS | Web-based database for facial expression analysis | [pdf] | [s2] | | 2a75f34663a60ab1b04a0049ed1d14335129e908 |
| moments_in_time | Moments in Time | Moments in Time Dataset: one million videos for event understanding | Moments in Time Dataset: one million videos for event understanding | [pdf] | [s2] | | 41976ebc8ab76d9a6861487c97cc7fcbe3b6015f |
| morph | MORPH Commercial | MORPH: A Longitudinal Image Database of Normal Adult Age-Progression | MORPH: a longitudinal image database of normal adult age-progression | [pdf] | [s2] | | 9055b155cbabdce3b98e16e5ac9c0edf00f9552f |
| morph_nc | MORPH-II | MORPH: A Longitudinal Image Database of Normal Adult Age-Progression | MORPH: a longitudinal image database of normal adult age-progression | [pdf] | [s2] | | 9055b155cbabdce3b98e16e5ac9c0edf00f9552f |
| mot | MOT | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics | [pdf] | [s2] | | 2258e01865367018ed6f4262c880df85b94959f8 |
| mot | MOT | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking | [pdf] | [s2] | | 27a2fad58dd8727e280f97036e0d2bc55ef5424c |
| mpi_large | Large MPI Facial Expression | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | [pdf] | [s2] | | ea050801199f98a1c7c1df6769f23f658299a3ae |
| mpi_small | Small MPI Facial Expression | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions | [pdf] | [s2] | | ea050801199f98a1c7c1df6769f23f658299a3ae |
| mpii_gaze | MPIIGaze | Appearance-based Gaze Estimation in the Wild | Appearance-based gaze estimation in the wild | [pdf] | [s2] | | 0df0d1adea39a5bef318b74faa37de7f3e00b452 |
| mpii_human_pose | MPII Human Pose | 2D Human Pose Estimation: New Benchmark and State of the Art Analysis | 2D Human Pose Estimation: New Benchmark and State of the Art Analysis | [pdf] | [s2] | | 3325860c0c82a93b2eac654f5324dd6a776f609e |
| mr2 | MR2 | The MR2: A multi-racial mega-resolution database of facial stimuli | The MR2: A multi-racial, mega-resolution database of facial stimuli. | [pdf] | [s2] | | 578d4ad74818086bb64f182f72e2c8bd31e3d426 |
| mrp_drone | MRP Drone | Investigating Open-World Person Re-identification Using a Drone | Investigating Open-World Person Re-identification Using a Drone | [pdf] | [s2] | | ad01687649d95cd5b56d7399a9603c4b8e2217d7 |
| msceleb | MsCeleb | MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition | MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition | [pdf] | [s2] | | 291265db88023e92bb8c8e6390438e5da148e8f5 |
| msmt_17 | MSMT17 | Person Transfer GAN to Bridge Domain Gap for Person Re-Identification | Person Transfer GAN to Bridge Domain Gap for Person Re-identification | [pdf] | [s2] | | a0cc5f73a37723a6dd465924143f1cb4976d0169 |
| mtfl | MTFL | Facial Landmark Detection by Deep Multi-task Learning | Facial Landmark Detection by Deep Multi-task Learning | [pdf] | [s2] | | 8a3c5507237957d013a0fe0f082cab7f757af6ee |
| mtfl | MTFL | Learning Deep Representation for Face Alignment with Auxiliary Attributes | Learning Deep Representation for Face Alignment with Auxiliary Attributes | [pdf] | [s2] | | a0fd85b3400c7b3e11122f44dc5870ae2de9009a |
| multi_pie | MULTIPIE | Multi-PIE | The CMU Pose, Illumination, and Expression (PIE) Database | [pdf] | [s2] | | 4d423acc78273b75134e2afd1777ba6d3a398973 |
| names_and_faces | News Dataset | Names and Faces | Names and faces in the news | [pdf] | [s2] | | 2fda164863a06a92d3a910b96eef927269aeb730 |
| nova_emotions | Novaemötions Dataset | Crowdsourcing facial expressions for affective-interaction | Crowdsourcing facial expressions for affective-interaction | [pdf] | [s2] | | c06b13d0ec3f5c43e2782cd22542588e233733c3 |
| orl | ORL | Parameterisation of a Stochastic Model for Human Face Identification | Parameterisation of a stochastic model for human face identification | [pdf] | [s2] | | 55206f0b5f57ce17358999145506cd01e570358c |
| pa_100k | PA-100K | HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis | HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis | [pdf] | [s2] | | f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44 |
| penn_fudan | Penn Fudan | Object Detection Combining Recognition and Segmentation | Object Detection Combining Recognition and Segmentation | [pdf] | [s2] | | 3394168ff0719b03ff65bcea35336a76b21fe5e4 |
| peta | PETA | Pedestrian Attribute Recognition At Far Distance | Pedestrian Attribute Recognition At Far Distance | [pdf] | [s2] | | 2a4bbee0b4cf52d5aadbbc662164f7efba89566c |
| pets | PETS 2017 | PETS 2017: Dataset and Challenge | PETS 2017: Dataset and Challenge | [pdf] | [s2] | | 22909dd19a0ec3b6065334cb5be5392cb24d839d |
| pilot_parliament | PPB | Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification | Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification | [pdf] | [s2] | | 18858cc936947fc96b5c06bbe3c6c2faa5614540 |
| pipa | PIPA | Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues | Beyond frontal faces: Improving Person Recognition using multiple cues | [pdf] | [s2] | | 0a85bdff552615643dd74646ac881862a7c7072d |
| pku_reid | PKU-Reid | Swiss-System Based Cascade Ranking for Gait-based Person Re-identification | Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification | [pdf] | [s2] | | f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f |
| pku_reid | PKU-Reid | Orientation driven bag of appearances for person re-identification | Orientation Driven Bag of Appearances for Person Re-identification | [pdf] | [s2] | | a7fe834a0af614ce6b50dc093132b031dd9a856b |
| precarious | Precarious | Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters | Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters | [pdf] | [s2] | | 9e5378e7b336c89735d3bb15cf67eff96f86d39a |
| prid | PRID | Person Re-Identification by Descriptive and Discriminative Classification | Person Re-identification by Descriptive and Discriminative Classification | [pdf] | [s2] | | 16c7c31a7553d99f1837fc6e88e77b5ccbb346b8 |
| prw | PRW | Person Re-identification in the Wild | Person Re-identification in the Wild | [pdf] | [s2] | | 0b84f07af44f964817675ad961def8a51406dd2e |
| psu | PSU | Vision-based Analysis of Small Groups in Pedestrian Crowds | Vision-Based Analysis of Small Groups in Pedestrian Crowds | [pdf] | [s2] | | 066000d44d6691d27202896691f08b27117918b9 |
| pubfig | PubFig | Attribute and Simile Classifiers for Face Verification | Attribute and simile classifiers for face verification | [pdf] | [s2] | | 759a3b3821d9f0e08e0b0a62c8b693230afc3f8d |
| put_face | Put Face | The PUT face database | The put face database | [pdf] | [s2] | | ae0aee03d946efffdc7af2362a42d3750e7dd48a |
| qmul_surv_face | QMUL-SurvFace | Surveillance Face Recognition Challenge | Surveillance Face Recognition Challenge | [pdf] | [s2] | | 2306b2a8fba28539306052764a77a0d0f5d1236a |
| rafd | RaFD | Presentation and validation of the Radboud Faces Database | Presentation and validation of the Radboud Faces Database | [pdf] | [s2] | | 3765df816dc5a061bc261e190acc8bdd9d47bec0 |
| raid | 43 | Consistent Re-identification in a Camera Network | Consistent Re-identification in a Camera Network | [pdf] | [s2] | | 09d78009687bec46e70efcf39d4612822e61cb8c |
| rap_pedestrian | RAP | A Richly Annotated Dataset for Pedestrian Attribute Recognition | A Richly Annotated Dataset for Pedestrian Attribute Recognition | [pdf] | [s2] | | 221c18238b829c12b911706947ab38fd017acef7 |
| reseed | ReSEED | ReSEED: Social Event dEtection Dataset | ReSEED: social event dEtection dataset | [pdf] | [s2] | | 54983972aafc8e149259d913524581357b0f91c3 |
| saivt | SAIVT SoftBio | A Database for Person Re-Identification in Multi-Camera Surveillance Networks | A Database for Person Re-Identification in Multi-Camera Surveillance Networks | [pdf] | [s2] | | 22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b |
| sarc3d | Sarc3D | SARC3D: a new 3D body model for People Tracking and Re-identification | SARC3D: A New 3D Body Model for People Tracking and Re-identification | [pdf] | [s2] | | e27ef52c641c2b5100a1b34fd0b819e84a31b4df |
| scface | SCface | SCface – surveillance cameras face database | SCface – surveillance cameras face database | [pdf] | [s2] | | 29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d |
| scut_fbp | SCUT-FBP | SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception | SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception | [pdf] | [s2] | | bd26dabab576adb6af30484183c9c9c8379bf2e0 |
| scut_head | SCUT HEAD | Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture | Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture | [pdf] | [s2] | | d3200d49a19a4a4e4e9745ee39649b65d80c834b |
| sdu_vid | SDU-VID | A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification | A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification | [pdf] | [s2] | | 3b4ec8af470948a72a6ed37a9fd226719a874ebc |
| sdu_vid | SDU-VID | Local descriptors encoded by Fisher vectors for person re-identification | Local Descriptors Encoded by Fisher Vectors for Person Re-identification | [pdf] | [s2] | | 46a01565e6afe7c074affb752e7069ee3bf2e4ef |
| sdu_vid | SDU-VID | Person reidentification by video ranking | Person Re-identification by Video Ranking | [pdf] | [s2] | | 98bb029afe2a1239c3fdab517323066f0957b81b |
| social_relation | Social Relation | Learning Social Relation Traits from Face Images | Learning Social Relation Traits from Face Images | [pdf] | [s2] | | 2a171f8d14b6b8735001a11c217af9587d095848 |
| soton | SOTON HiD | On a Large Sequence-Based Human Gait Database | On a Large Sequence-Based Human Gait Database | [pdf] | [s2] | | 4f93cd09785c6e77bf4bc5a788e079df524c8d21 |
| sports_videos_in_the_wild | SVW | Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis | Sports Videos in the Wild (SVW): A video dataset for sports analysis | [pdf] | [s2] | | 1a40092b493c6b8840257ab7f96051d1a4dbfeb2 |
| stair_actions | STAIR Action | STAIR Actions: A Video Dataset of Everyday Home Actions | STAIR Actions: A Video Dataset of Everyday Home Actions | [pdf] | [s2] | | d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9 |
| stanford_drone | Stanford Drone | Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes | Social LSTM: Human Trajectory Prediction in Crowded Spaces | [pdf] | [s2] | | 570f37ed63142312e6ccdf00ecc376341ec72b9f |
| stickmen_buffy | Buffy Stickmen | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | [pdf] | [s2] | | 4b1d23d17476fcf78f4cbadf69fb130b1aa627c0 |
| stickmen_buffy | Buffy Stickmen | Learning to Parse Images of Articulated Objects | Learning to parse images of articulated bodies | [pdf] | [s2] | | 6dd0597f8513dc100cd0bc1b493768cde45098a9 |
| stickmen_family | We Are Family Stickmen | We Are Family: Joint Pose Estimation of Multiple Persons | We Are Family: Joint Pose Estimation of Multiple Persons | [pdf] | [s2] | | 0dc11a37cadda92886c56a6fb5191ded62099c28 |
| stickmen_pascal | Stickmen PASCAL | Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation | Learning to parse images of articulated bodies | [pdf] | [s2] | | 6dd0597f8513dc100cd0bc1b493768cde45098a9 |
| stickmen_pascal | Stickmen PASCAL | Learning to Parse Images of Articulated Objects | Learning to parse images of articulated bodies | [pdf] | [s2] | | 6dd0597f8513dc100cd0bc1b493768cde45098a9 |
| sun_attributes | SUN | The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding | The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding | [pdf] | [s2] | | 66e6f08873325d37e0ec20a4769ce881e04e964e |
| svs | SVS | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | Pedestrian Attribute Classification in Surveillance: Database and Evaluation | [pdf] | [s2] | | 488e475eeb3bb39a145f23ede197cd3620f1d98a |
| texas_3dfrd | Texas 3DFRD | Anthropometric 3D Face Recognition | Anthropometric 3D Face Recognition | [pdf] | [s2] | | 2ce2560cf59db59ce313bbeb004e8ce55c5ce928 |
| texas_3dfrd | Texas 3DFRD | Texas 3D Face Recognition Database | Texas 3D Face Recognition Database | [pdf] | [s2] | | 4d58f886f5150b2d5e48fd1b5a49e09799bf895d |
| tiny_faces | TinyFace | Low-Resolution Face Recognition | Low-Resolution Face Recognition | [pdf] | [s2] | | 8990cdce3f917dad622e43e033db686b354d057c |
| tiny_images | #N/A | 80 million tiny images: a large dataset for non-parametric object and scene recognition | 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition | [pdf] | [s2] | | 31b58ced31f22eab10bd3ee2d9174e7c14c27c01 |
| tisi | Times Square Intersection | Video Synopsis by Heterogeneous Multi-source Correlation | Video Synopsis by Heterogeneous Multi-source Correlation | [pdf] | [s2] | | b6c293f0420f7e945b5916ae44269fb53e139275 |
| tisi | Times Square Intersection | Learning from Multiple Sources for Video Summarisation | Learning from Multiple Sources for Video Summarisation | [pdf] | [s2] | | 287ddcb3db5562235d83aee318f318b8d5e43fb1 |
| oxford_town_centre | TownCentre | Stable Multi-Target Tracking in Real-Time Surveillance Video | Stable multi-target tracking in real-time surveillance video | [pdf] | [s2] | | 9361b784e73e9238d5cefbea5ac40d35d1e3103f |
| tud_brussels | TUD-Brussels | Multi-Cue Onboard Pedestrian Detection | Multi-cue onboard pedestrian detection | [pdf] | [s2] | | 6ad5a38df8dd4cdddd74f31996ce096d41219f72 |
| tud_campus | TUD-Campus | People-Tracking-by-Detection and People-Detection-by-Tracking | People-tracking-by-detection and people-detection-by-tracking | [pdf] | [s2] | | 3316521a5527c7700af8ae6aef32a79a8b83672c |
| tud_crossing | TUD-Crossing | People-Tracking-by-Detection and People-Detection-by-Tracking | People-tracking-by-detection and people-detection-by-tracking | [pdf] | [s2] | | 3316521a5527c7700af8ae6aef32a79a8b83672c |
| tud_motionpairs | TUD-Motionparis | Multi-Cue Onboard Pedestrian Detection | Multi-cue onboard pedestrian detection | [pdf] | [s2] | | 6ad5a38df8dd4cdddd74f31996ce096d41219f72 |
| tud_pedestrian | TUD-Pedestrian | People-Tracking-by-Detection and People-Detection-by-Tracking | People-tracking-by-detection and people-detection-by-tracking | [pdf] | [s2] | | 3316521a5527c7700af8ae6aef32a79a8b83672c |
| tvhi | TVHI | High Five: Recognising human interactions in TV shows | High Five: Recognising human interactions in TV shows | [pdf] | [s2] | | 3cd40bfa1ff193a96bde0207e5140a399476466c |
| uccs | UCCS | Large scale unconstrained open set face database | Large scale unconstrained open set face database | [pdf] | [s2] | | 07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1 |
| uccs | UCCS | Unconstrained Face Detection and Open-Set Face Recognition Challenge | Unconstrained Face Detection and Open-Set Face Recognition Challenge | [pdf] | [s2] | | d4f1eb008eb80595bcfdac368e23ae9754e1e745 |
| ucf_101 | UCF101 | UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild | UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild | [pdf] | [s2] | | b5f2846a506fc417e7da43f6a7679146d99c5e96 |
| ucf_crowd | UCF-CC-50 | Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images | Multi-source Multi-scale Counting in Extremely Dense Crowd Images | [pdf] | [s2] | | 32c801cb7fbeb742edfd94cccfca4934baec71da |
| ucf_selfie | UCF Selfie | How to Take a Good Selfie? | How to Take a Good Selfie? | [pdf] | [s2] | | 041d3eedf5e45ce5c5229f0181c5c576ed1fafd6 |
| ufdd | UFDD | Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results | Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results | [pdf] | [s2] | | 3531332efe19be21e7401ba1f04570a142617236 |
| umb | UMB | UMB-DB: A Database of Partially Occluded 3D Faces | UMB-DB: A database of partially occluded 3D faces | [pdf] | [s2] | | 16e8b0a1e8451d5f697b94c0c2b32a00abee1d52 |
| umd_faces | UMD | UMDFaces: An Annotated Face Dataset for Training Deep Networks | UMDFaces: An annotated face dataset for training deep networks | [pdf] | [s2] | | 31b05f65405534a696a847dd19c621b7b8588263 |
| umd_faces | UMD | The Do's and Don'ts for CNN-based Face Verification | The Do’s and Don’ts for CNN-Based Face Verification | [pdf] | [s2] | | 71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6 |
| urban_tribes | Urban Tribes | From Bikers to Surfers: Visual Recognition of Urban Tribes | From Bikers to Surfers: Visual Recognition of Urban Tribes | [pdf] | [s2] | | 774cbb45968607a027ae4729077734db000a1ec5 |
| vgg_celebs_in_places | CIP | Faces in Places: Compound Query Retrieval | Faces in Places: compound query retrieval | [pdf] | [s2] | | 7ebb153704706e457ab57b432793d2b6e5d12592 |
| vgg_faces | VGG Face | Deep Face Recognition | Deep Face Recognition | [pdf] | [s2] | | 162ea969d1929ed180cc6de9f0bf116993ff6e06 |
| vgg_faces2 | VGG Face2 | VGGFace2: A dataset for recognising faces across pose and age | VGGFace2: A Dataset for Recognising Faces across Pose and Age | [pdf] | [s2] | | 70c59dc3470ae867016f6ab0e008ac8ba03774a1 |
| viper | VIPeR | Evaluating Appearance Models for Recognition, Reacquisition, and Tracking | Evaluating Appearance Models for Recognition, Reacquisition, and Tracking | [pdf] | [s2] | | 6273b3491e94ea4dd1ce42b791d77bdc96ee73a8 |
| voc | VOC | The PASCAL Visual Object Classes (VOC) Challenge | The Pascal Visual Object Classes (VOC) Challenge | [pdf] | [s2] | | 0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a |
| voxceleb2 | VoxCeleb2 | VoxCeleb2: Deep Speaker Recognition | VoxCeleb2: Deep Speaker Recognition. | [pdf] | [s2] | | 8875ae233bc074f5cd6c4ebba447b536a7e847a5 |
| vqa | VQA | VQA: Visual Question Answering | VQA: Visual Question Answering | [pdf] | [s2] | | 01959ef569f74c286956024866c1d107099199f7 |
| wider | WIDER | Recognize Complex Events from Static Images by Fusing Deep Channels | Recognize complex events from static images by fusing deep channels | [pdf] | [s2] | | 356b431d4f7a2a0a38cf971c84568207dcdbf189 |
| wider_attribute | WIDER Attribute | Human Attribute Recognition by Deep Hierarchical Contexts | Human Attribute Recognition by Deep Hierarchical Contexts | [pdf] | [s2] | | 44d23df380af207f5ac5b41459c722c87283e1eb |
| wider_face | WIDER FACE | WIDER FACE: A Face Detection Benchmark | WIDER FACE: A Face Detection Benchmark | [pdf] | [s2] | | 52d7eb0fbc3522434c13cc247549f74bb9609c5d |
| wildtrack | WildTrack | WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection | WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection | [pdf] | [s2] | | 36bccfb2ad847096bc76777e544f305813cd8f5b |
| wlfdb | WLFDB | WLFDB: Weakly Labeled Face Databases | WLFDB : Weakly Labeled Face Databases | [pdf] | [s2] | | 5ad4e9f947c1653c247d418f05dad758a3f9277b |
| yale_faces | YaleFaces | From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose | From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose | [pdf] | [s2] | | 18c72175ddbb7d5956d180b65a96005c100f6014 |
| yale_faces | YaleFaces | Acquiring Linear Subspaces for Face Recognition under Variable Lighting | Acquiring linear subspaces for face recognition under variable lighting | [pdf] | [s2] | | 2ad0ee93d029e790ebb50574f403a09854b65b7e |
| yawdd | YawDD | YawDD: A Yawning Detection Dataset | YawDD: a yawning detection dataset | [pdf] | [s2] | | a94cae786d515d3450d48267e12ca954aab791c4 |
| yfcc_100m | YFCC100M | YFCC100M: The New Data in Multimedia Research | YFCC100M: the new data in multimedia research | [pdf] | [s2] | | 010f0f4929e6a6644fb01f0e43820f91d0fad292 |
| york_3d | UOY 3D Face Database | Three-Dimensional Face Recognition: An Eigensurface Approach | Three-dimensional face recognition: an eigensurface approach | [pdf] | [s2] | | 19d1b811df60f86cbd5e04a094b07f32fff7a32a |
| youtube_faces | YouTubeFaces | Face Recognition in Unconstrained Videos with Matched Background Similarity | Face recognition in unconstrained videos with matched background similarity | [pdf] | [s2] | | 560e0e58d0059259ddf86fcec1fa7975dee6a868 |
| youtube_poses | YouTube Pose | Personalizing Human Video Pose Estimation | Personalizing Human Video Pose Estimation | [pdf] | [s2] | | 1c2802c2199b6d15ecefe7ba0c39bfe44363de38 |
| flickr_faces | FFHQ | A Style-Based Generator Architecture for Generative Adversarial Networks | A Style-Based Generator Architecture for Generative Adversarial Networks | [pdf] | [s2] | | f038758e85c9ee6fee68a4f3992d0303b5c93efd |