diff options
Diffstat (limited to 'site/datasets/unknown/megaface.json')
| -rw-r--r-- | site/datasets/unknown/megaface.json | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/site/datasets/unknown/megaface.json b/site/datasets/unknown/megaface.json index e339cbda..4d9d1ce4 100644 --- a/site/datasets/unknown/megaface.json +++ b/site/datasets/unknown/megaface.json @@ -1 +1 @@ -{"id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "citations": [{"id": "380dd0ddd5d69adc52defc095570d1c22952f5cc", "title": "Improving Smiling Detection with Race and Gender Diversity", "year": "2017", "pdf": []}, {"id": "57178b36c21fd7f4529ac6748614bb3374714e91", "title": "IARPA Janus Benchmark - C: Face Dataset and Protocol", "year": "2018", "pdf": []}, {"id": "06bd34951305d9f36eb29cf4532b25272da0e677", "title": "A Fast and Accurate System for Face Detection, Identification, and Verification", "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.07586.pdf"]}, {"id": "dc13229afbbc8b7a31ed5adfe265d971850c0976", "title": "Learning from Millions of 3 D Scans for Large-scale 3 D Face Recognition", "year": "2017", "pdf": []}, {"id": "9a10845115794117485fc84f9b9e6ada2a7d2b00", "title": "Eye In-painting with Exemplar Generative Adversarial Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1712.03999.pdf"]}, {"id": "2306b2a8fba28539306052764a77a0d0f5d1236a", "title": "Surveillance Face Recognition Challenge", "year": "2018", "pdf": ["https://arxiv.org/pdf/1804.09691.pdf"]}, {"id": "d1a43737ca8be02d65684cf64ab2331f66947207", "title": "IJB \u2013 S : IARPA Janus Surveillance Video Benchmark \u2217", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/d1a4/3737ca8be02d65684cf64ab2331f66947207.pdf"]}, {"id": "9ea37d031a8f112292c0d0f8d731b837462714e9", "title": "Face Recognition: From Traditional to Deep Learning Methods", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.00116.pdf"]}, {"id": "f47518fcd69cdbb43dc88fe5259f4f4c61921313", "title": "A Compact Embedding for Facial Expression Similarity", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.11283.pdf"]}, {"id": "b4f3e9fc0a2b40595ae0a625d1d768a57a7c2eba", "title": "Recognizing Disguised Faces in the Wild", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.08837.pdf"]}, {"id": "1ffe20eb32dbc4fa85ac7844178937bba97f4bf0", "title": "Face Clustering: Representation and Pairwise Constraints", "year": "2018", "pdf": ["https://arxiv.org/pdf/1706.05067.pdf"]}, {"id": "8de1c724a42d204c0050fe4c4b4e81a675d7f57c", "title": "Deep Face Recognition: A Survey", "year": "2018", "pdf": ["https://talhassner.github.io/home/projects/DeepFaceSurvey/Masietal2018deepfacesurvey.pdf"]}, {"id": "c76251049b370f8258d6bbb944c696c30b8bbb85", "title": "Clothing Change Aware Person Identification", "year": "2018", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf"]}, {"id": "7323b594d3a8508f809e276aa2d224c4e7ec5a80", "title": "An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification", "year": "2018", "pdf": ["https://arxiv.org/pdf/1808.05508.pdf"]}, {"id": "0a23bdc55fb0d04acdac4d3ea0a9994623133562", "title": "Large-scale Bisample Learning on ID vs. Spot Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.03018.pdf"]}, {"id": "1174b869c325222c3446d616975842e8d2989cf2", "title": "CosFace: Large Margin Cosine Loss for Deep Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.09414.pdf"]}, {"id": "65984ea40c3b17bb8965c215b61972cd660f61a7", "title": "Doppelganger Mining for Face Representation Learning", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w27/Smirnov_Doppelganger_Mining_for_ICCV_2017_paper.pdf"]}, {"id": "4739b47af26137ec52bbfb582e6f37e9e9f5aba0", "title": "Hard Example Mining with Auxiliary Embeddings", "year": "2018", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Smirnov_Hard_Example_Mining_CVPR_2018_paper.pdf"]}, {"id": "e4232e8fd566a7289ccb33f732c9093c9beb84a6", "title": "UHDB31: A Dataset for Better Understanding Face Recognition Across Pose and Illumination Variation", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Le_UHDB31_A_Dataset_ICCV_2017_paper.pdf"]}, {"id": "cfd4004054399f3a5f536df71f9b9987f060f434", "title": "Person Recognition in Social Media Photos", "year": "2018", "pdf": ["https://arxiv.org/pdf/1710.03224.pdf"]}, {"id": "27da432cf2b9129dce256e5bf7f2f18953eef5a5", "title": "Face Recognition in Low Quality Images: A Survey", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.11519.pdf"]}, {"id": "3d85cf942efda695347c7d95485fcd1e6796ee3a", "title": "Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.00112.pdf"]}, {"id": "3827f1cab643a57e3cd22fbffbf19dd5e8a298a8", "title": "One-Shot Face Recognition via Generative Learning", "year": "2018", "pdf": []}, {"id": "b612bbd751d94996a02b85829e0b2ac0511350ff", "title": "Learning Representations for Utility and Privacy : An Information-Theoretic Based Approach", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/b612/bbd751d94996a02b85829e0b2ac0511350ff.pdf"]}, {"id": "9ce12c9f1d1661f56908edc8ef3848e91b24d557", "title": "Query Adaptive Late Fusion for Image Retrieval", "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.13103.pdf"]}, {"id": "1b4b3d0ce900996a6da8928e16370e21d15ed83e", "title": "A Review of Performance Evaluation on 2D Face Databases", "year": "2017", "pdf": []}, {"id": "4e1a65f3c3d9cfeecab898affbe0b47a9b6c9157", "title": "DIY Human Action Dataset Generation", "year": "2018", "pdf": []}, {"id": "7d0b7a42368d7fb78ade5e21cad713b5c5611ee4", "title": "SensitiveNets: Learning Agnostic Representations with Application to Face Recognition", "year": "2019", "pdf": ["https://arxiv.org/pdf/1902.00334.pdf"]}, {"id": "2c75658b080a9baaac20db39af86016ffa36f6f0", "title": "Seeing Voices and Hearing Faces: Cross-Modal Biometric Matching", "year": "2018", "pdf": []}, {"id": "8395cf3535a6628c3bdc9b8d0171568d551f5ff0", "title": "Entropy Non-increasing Games for the Improvement of Dataflow Programming", "year": "2017", "pdf": ["https://arxiv.org/pdf/1702.04389.pdf"]}, {"id": "ff01bc3f49130d436fca24b987b7e3beedfa404d", "title": "Fuzzy System-Based Face Detection Robust to In-Plane Rotation Based on Symmetrical Characteristics of a Face", "year": "2016", "pdf": ["http://www.mdpi.com/2073-8994/8/8/75/pdf"]}, {"id": "380d5138cadccc9b5b91c707ba0a9220b0f39271", "title": "Deep Imbalanced Learning for Face Recognition and Attribute Prediction", "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.00194.pdf"]}, {"id": "0ab7cff2ccda7269b73ff6efd9d37e1318f7db25", "title": "Facial Coding Scheme Reference 1 Craniofacial Distances", "year": "2019", "pdf": []}, {"id": "b1b7603a70860cbe5ff7b963976b5e6f780c88fc", "title": "A Deep Face Identification Network Enhanced by Facial Attributes Prediction", "year": "2018", "pdf": []}, {"id": "48499deeaa1e31ac22c901d115b8b9867f89f952", "title": "Interim Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/4849/9deeaa1e31ac22c901d115b8b9867f89f952.pdf"]}, {"id": "6bb95a0f3668cd36407c85899b71c9fe44bf9573", "title": "Face attribute prediction using off-the-shelf CNN features", "year": "2016", "pdf": ["https://arxiv.org/pdf/1602.03935.pdf"]}, {"id": "3c0fffd4cdbfef4ccd92d528d8b8a60ab0929827", "title": "An Empirical Study of Face Recognition under Variations", "year": "2018", "pdf": []}, {"id": "ddfde808af8dc8b737d115869d6cca780d050884", "title": "Minimum Margin Loss for Deep Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.06741.pdf"]}, {"id": "c607572fd2594ca83f732c9790fd590da9e69eb1", "title": "Comparative Evaluation of Deep Architectures for Face Recognition in Unconstrained Environment ( FRUE )", "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/c607/572fd2594ca83f732c9790fd590da9e69eb1.pdf"]}, {"id": "e3d76f1920c5bf4a60129516abb4a2d8683e48ae", "title": "I Know That Person: Generative Full Body and Face De-identification of People in Images", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2017_workshops/w16/papers/Kalafatic_I_Know_That_CVPR_2017_paper.pdf"]}, {"id": "18858cc936947fc96b5c06bbe3c6c2faa5614540", "title": "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification", "year": "2018", "pdf": ["http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a-supp.pdf", "https://dam-prod.media.mit.edu/x/2018/02/06/Gender%20Shades%20Intersectional%20Accuracy%20Disparities.pdf"]}, {"id": "67a9659de0bf671fafccd7f39b7587f85fb6dfbd", "title": "Ring Loss: Convex Feature Normalization for Face Recognition", "year": "2018", "pdf": []}, {"id": "94f74c6314ffd02db581e8e887b5fd81ce288dbf", "title": "A Light CNN for Deep Face Representation With Noisy Labels", "year": "2018", "pdf": ["https://arxiv.org/pdf/1511.02683.pdf"]}, {"id": "a2344004f0e1409c0c9473d071a5cfd74bff0a5d", "title": "Learnable PINs: Cross-modal Embeddings for Person Identity", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.00833.pdf"]}, {"id": "d44a93027208816b9e871101693b05adab576d89", "title": "On the Capacity of Face Representation", "year": "2017", "pdf": ["https://arxiv.org/pdf/1709.10433.pdf"]}, {"id": "c924137ca87e8b4e1557465405744f8b639b16fc", "title": "Seeding Deep Learning using Wireless Localization", "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.10242.pdf"]}, {"id": "6cacda04a541d251e8221d70ac61fda88fb61a70", "title": "One-shot Face Recognition by Promoting Underrepresented Classes", "year": "2017", "pdf": ["https://arxiv.org/pdf/1707.05574.pdf"]}, {"id": "d00787e215bd74d32d80a6c115c4789214da5edb", "title": "Faster and Lighter Online Sparse Dictionary Learning Project report", "year": "", "pdf": ["https://pdfs.semanticscholar.org/d007/87e215bd74d32d80a6c115c4789214da5edb.pdf"]}, {"id": "934f69d038721540c96093cae634573b89d7d714", "title": "DeSTNet : Densely Fused Spatial Transformer Networks 1", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/934f/69d038721540c96093cae634573b89d7d714.pdf"]}, {"id": "1b805f8cbcffe7f5e2cc7af86b5649330b15298d", "title": "Heterogeneous Face Recognition Using Domain Specific Units", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/1b80/5f8cbcffe7f5e2cc7af86b5649330b15298d.pdf"]}, {"id": "9ba4a5e0b7bf6e26563d294f1f3de44d95b7f682", "title": "To Frontalize or Not to Frontalize: Do We Really Need Elaborate Pre-processing to Improve Face Recognition?", "year": "2018", "pdf": ["http://docs.wixstatic.com/ugd/445e27_b7f15ceb15d34e45836f98d9eeba9a78.pdf", "https://arxiv.org/pdf/1610.04823v1.pdf"]}, {"id": "a52a69bf304d49fba6eac6a73c5169834c77042d", "title": "Margin Loss: Making Faces More Separable", "year": "2018", "pdf": []}, {"id": "a50fa5048c61209149de0711b5f1b1806b43da00", "title": "Deep Features for Recognizing Disguised Faces in the Wild", "year": "2018", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Bansal_Deep_Features_for_CVPR_2018_paper.pdf"]}, {"id": "9e1b0f50417867317a8cb8fe35c6b2617ad9641e", "title": "Diversity in Faces", "year": "2019", "pdf": ["https://arxiv.org/pdf/1901.10436.pdf"]}, {"id": "ca5e9973a4494c608548f639eb9a391f6235d4f0", "title": "Robust RGB-D Face Recognition Using Attribute-Aware Loss", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.09847.pdf"]}, {"id": "d2eb1079552fb736e3ba5e494543e67620832c52", "title": "DeSTNet: Densely Fused Spatial Transformer Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.04050.pdf"]}, {"id": "91e17338a12b5e570907e816bff296b13177971e", "title": "Towards open-set face recognition using hashing functions", "year": "2017", "pdf": ["http://homepages.dcc.ufmg.br/~william/papers/paper_2017_IJCB.pdf"]}, {"id": "cfb8bc66502fb5f941ecdb22aec1fdbfdb73adce", "title": "Git Loss for Deep Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.08512.pdf"]}, {"id": "31b05f65405534a696a847dd19c621b7b8588263", "title": "UMDFaces: An annotated face dataset for training deep networks", "year": "2017", "pdf": []}, {"id": "dde5125baefa1141f1ed50479a3fd67c528a965f", "title": "Synthesizing Normalized Faces from Facial Identity Features", "year": "2017", "pdf": ["https://arxiv.org/pdf/1701.04851.pdf"]}, {"id": "75249ebb85b74e8932496272f38af274fbcfd696", "title": "Face Identification in Large Galleries", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/7524/9ebb85b74e8932496272f38af274fbcfd696.pdf"]}, {"id": "9ebf00558968a7ca6e130b61e70bbe2e24706da4", "title": "Ongoing face recognition vendor test (FRVT) part 2:: identification", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/5cfe/f7aedc2651d7754911a7a5cf9ee4edbe876a.pdf"]}, {"id": "59fc69b3bc4759eef1347161e1248e886702f8f7", "title": "Final Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/59fc/69b3bc4759eef1347161e1248e886702f8f7.pdf"]}, {"id": "5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952", "title": "Learning towards Minimum Hyperspherical Energy", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.09298.pdf"]}, {"id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "title": "Level Playing Field for Million Scale Face Recognition", "year": "2017", "pdf": []}, {"id": "d818568838433a6d6831adde49a58cef05e0c89f", "title": "AgeDB: The First Manually Collected, In-the-Wild Age Database", "year": "2017", "pdf": ["http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf", "http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf", "https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf"]}, {"id": "4f0b641860d90dfa4c185670bf636149a2b2b717", "title": "Improve Cross-Domain Face Recognition with IBN-block", "year": "2018", "pdf": []}, {"id": "284d8ffb2f2d3bc9f793b82f8b7f75f2751b05d7", "title": "Disguised Faces in the Wild", "year": "2018", "pdf": ["http://iab-rubric.org/papers/2018_CVPRW_disguised-faces-wild.pdf", "http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Kushwaha_Disguised_Faces_in_CVPR_2018_paper.pdf"]}, {"id": "d31328b12eef33e7722b8e5505d0f9d9abe2ffd9", "title": "Deep Unsupervised Domain Adaptation for Face Recognition", "year": "2018", "pdf": []}, {"id": "2b2896f41480399e7182acfa9466a3f915885387", "title": "CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing", "year": "2018", "pdf": ["https://arxiv.org/pdf/1812.00408.pdf"]}, {"id": "3baa3d5325f00c7edc1f1427fcd5bdc6a420a63f", "title": "Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss", "year": "2018", "pdf": ["https://arxiv.org/pdf/1707.07923.pdf"]}, {"id": "8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b", "title": "Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning", "year": "2017", "pdf": ["https://arxiv.org/pdf/1711.10520.pdf"]}, {"id": "28cd46a078e8fad370b1aba34762a874374513a5", "title": "cvpaper.challenge in 2016: Futuristic Computer Vision through 1, 600 Papers Survey", "year": "2017", "pdf": ["https://arxiv.org/pdf/1707.06436.pdf"]}, {"id": "f60070d3a4d333aa1436e4c372b1feb5b316a7ba", "title": "Face Recognition via Centralized Coordinate Learning", "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.05678.pdf"]}, {"id": "726f76f11e904d7fcb12736c276a0b00eb5cde49", "title": "A Performance Comparison of Loss Functions for Deep Face Recognition", "year": "2019", "pdf": ["https://arxiv.org/pdf/1901.05903.pdf"]}, {"id": "53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f", "title": "Accelerated Training for Massive Classification via Dynamic Class Selection", "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.01687.pdf"]}, {"id": "173657da03e3249f4e47457d360ab83b3cefbe63", "title": "HKU-Face : A Large Scale Dataset for Deep Face Recognition Final Report", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/1736/57da03e3249f4e47457d360ab83b3cefbe63.pdf"]}]}
\ No newline at end of file +{"id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "citations": [{"id": "380dd0ddd5d69adc52defc095570d1c22952f5cc", "title": "Improving Smiling Detection with Race and Gender Diversity", "year": "2017", "pdf": []}, {"id": "57178b36c21fd7f4529ac6748614bb3374714e91", "title": "IARPA Janus Benchmark - C: Face Dataset and Protocol", "year": "2018", "pdf": []}, {"id": "06bd34951305d9f36eb29cf4532b25272da0e677", "title": "A Fast and Accurate System for Face Detection, Identification, and Verification", "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.07586.pdf"]}, {"id": "dc13229afbbc8b7a31ed5adfe265d971850c0976", "title": "Learning from Millions of 3 D Scans for Large-scale 3 D Face Recognition", "year": "2017", "pdf": []}, {"id": "9a10845115794117485fc84f9b9e6ada2a7d2b00", "title": "Eye In-painting with Exemplar Generative Adversarial Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1712.03999.pdf"]}, {"id": "9ea37d031a8f112292c0d0f8d731b837462714e9", "title": "Face Recognition: From Traditional to Deep Learning Methods", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.00116.pdf"]}, {"id": "f47518fcd69cdbb43dc88fe5259f4f4c61921313", "title": "A Compact Embedding for Facial Expression Similarity", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.11283.pdf"]}, {"id": "b4f3e9fc0a2b40595ae0a625d1d768a57a7c2eba", "title": "Recognizing Disguised Faces in the Wild", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.08837.pdf"]}, {"id": "1ffe20eb32dbc4fa85ac7844178937bba97f4bf0", "title": "Face Clustering: Representation and Pairwise Constraints", "year": "2018", "pdf": ["https://arxiv.org/pdf/1706.05067.pdf"]}, {"id": "8de1c724a42d204c0050fe4c4b4e81a675d7f57c", "title": "Deep Face Recognition: A Survey", "year": "2018", "pdf": ["https://talhassner.github.io/home/projects/DeepFaceSurvey/Masietal2018deepfacesurvey.pdf"]}, {"id": "c76251049b370f8258d6bbb944c696c30b8bbb85", "title": "Clothing Change Aware Person Identification", "year": "2018", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w41/Xue_Clothing_Change_Aware_CVPR_2018_paper.pdf"]}, {"id": "7323b594d3a8508f809e276aa2d224c4e7ec5a80", "title": "An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification", "year": "2018", "pdf": ["https://arxiv.org/pdf/1808.05508.pdf"]}, {"id": "0a23bdc55fb0d04acdac4d3ea0a9994623133562", "title": "Large-scale Bisample Learning on ID vs. Spot Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.03018.pdf"]}, {"id": "1174b869c325222c3446d616975842e8d2989cf2", "title": "CosFace: Large Margin Cosine Loss for Deep Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.09414.pdf"]}, {"id": "65984ea40c3b17bb8965c215b61972cd660f61a7", "title": "Doppelganger Mining for Face Representation Learning", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w27/Smirnov_Doppelganger_Mining_for_ICCV_2017_paper.pdf"]}, {"id": "4739b47af26137ec52bbfb582e6f37e9e9f5aba0", "title": "Hard Example Mining with Auxiliary Embeddings", "year": "2018", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Smirnov_Hard_Example_Mining_CVPR_2018_paper.pdf"]}, {"id": "e4232e8fd566a7289ccb33f732c9093c9beb84a6", "title": "UHDB31: A Dataset for Better Understanding Face Recognition Across Pose and Illumination Variation", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Le_UHDB31_A_Dataset_ICCV_2017_paper.pdf"]}, {"id": "cfd4004054399f3a5f536df71f9b9987f060f434", "title": "Person Recognition in Social Media Photos", "year": "2018", "pdf": ["https://arxiv.org/pdf/1710.03224.pdf"]}, {"id": "27da432cf2b9129dce256e5bf7f2f18953eef5a5", "title": "Face Recognition in Low Quality Images: A Survey", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.11519.pdf"]}, {"id": "3d85cf942efda695347c7d95485fcd1e6796ee3a", "title": "Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.00112.pdf"]}, {"id": "3827f1cab643a57e3cd22fbffbf19dd5e8a298a8", "title": "One-Shot Face Recognition via Generative Learning", "year": "2018", "pdf": []}, {"id": "b612bbd751d94996a02b85829e0b2ac0511350ff", "title": "Learning Representations for Utility and Privacy : An Information-Theoretic Based Approach", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/b612/bbd751d94996a02b85829e0b2ac0511350ff.pdf"]}, {"id": "9ce12c9f1d1661f56908edc8ef3848e91b24d557", "title": "Query Adaptive Late Fusion for Image Retrieval", "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.13103.pdf"]}, {"id": "1b4b3d0ce900996a6da8928e16370e21d15ed83e", "title": "A Review of Performance Evaluation on 2D Face Databases", "year": "2017", "pdf": []}, {"id": "4e1a65f3c3d9cfeecab898affbe0b47a9b6c9157", "title": "DIY Human Action Dataset Generation", "year": "2018", "pdf": []}, {"id": "7d0b7a42368d7fb78ade5e21cad713b5c5611ee4", "title": "SensitiveNets: Learning Agnostic Representations with Application to Face Recognition", "year": "2019", "pdf": ["https://arxiv.org/pdf/1902.00334.pdf"]}, {"id": "2c75658b080a9baaac20db39af86016ffa36f6f0", "title": "Seeing Voices and Hearing Faces: Cross-Modal Biometric Matching", "year": "2018", "pdf": []}, {"id": "8395cf3535a6628c3bdc9b8d0171568d551f5ff0", "title": "Entropy Non-increasing Games for the Improvement of Dataflow Programming", "year": "2017", "pdf": ["https://arxiv.org/pdf/1702.04389.pdf"]}, {"id": "ff01bc3f49130d436fca24b987b7e3beedfa404d", "title": "Fuzzy System-Based Face Detection Robust to In-Plane Rotation Based on Symmetrical Characteristics of a Face", "year": "2016", "pdf": ["http://www.mdpi.com/2073-8994/8/8/75/pdf"]}, {"id": "380d5138cadccc9b5b91c707ba0a9220b0f39271", "title": "Deep Imbalanced Learning for Face Recognition and Attribute Prediction", "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.00194.pdf"]}, {"id": "0ab7cff2ccda7269b73ff6efd9d37e1318f7db25", "title": "Facial Coding Scheme Reference 1 Craniofacial Distances", "year": "2019", "pdf": []}, {"id": "b1b7603a70860cbe5ff7b963976b5e6f780c88fc", "title": "A Deep Face Identification Network Enhanced by Facial Attributes Prediction", "year": "2018", "pdf": []}, {"id": "48499deeaa1e31ac22c901d115b8b9867f89f952", "title": "Interim Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/4849/9deeaa1e31ac22c901d115b8b9867f89f952.pdf"]}, {"id": "6bb95a0f3668cd36407c85899b71c9fe44bf9573", "title": "Face attribute prediction using off-the-shelf CNN features", "year": "2016", "pdf": ["https://arxiv.org/pdf/1602.03935.pdf"]}, {"id": "3c0fffd4cdbfef4ccd92d528d8b8a60ab0929827", "title": "An Empirical Study of Face Recognition under Variations", "year": "2018", "pdf": []}, {"id": "ddfde808af8dc8b737d115869d6cca780d050884", "title": "Minimum Margin Loss for Deep Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.06741.pdf"]}, {"id": "c607572fd2594ca83f732c9790fd590da9e69eb1", "title": "Comparative Evaluation of Deep Architectures for Face Recognition in Unconstrained Environment ( FRUE )", "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/c607/572fd2594ca83f732c9790fd590da9e69eb1.pdf"]}, {"id": "e3d76f1920c5bf4a60129516abb4a2d8683e48ae", "title": "I Know That Person: Generative Full Body and Face De-identification of People in Images", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2017_workshops/w16/papers/Kalafatic_I_Know_That_CVPR_2017_paper.pdf"]}, {"id": "18858cc936947fc96b5c06bbe3c6c2faa5614540", "title": "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification", "year": "2018", "pdf": ["http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a-supp.pdf", "https://dam-prod.media.mit.edu/x/2018/02/06/Gender%20Shades%20Intersectional%20Accuracy%20Disparities.pdf"]}, {"id": "67a9659de0bf671fafccd7f39b7587f85fb6dfbd", "title": "Ring Loss: Convex Feature Normalization for Face Recognition", "year": "2018", "pdf": []}, {"id": "94f74c6314ffd02db581e8e887b5fd81ce288dbf", "title": "A Light CNN for Deep Face Representation With Noisy Labels", "year": "2018", "pdf": ["https://arxiv.org/pdf/1511.02683.pdf"]}, {"id": "a2344004f0e1409c0c9473d071a5cfd74bff0a5d", "title": "Learnable PINs: Cross-modal Embeddings for Person Identity", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.00833.pdf"]}, {"id": "d44a93027208816b9e871101693b05adab576d89", "title": "On the Capacity of Face Representation", "year": "2017", "pdf": ["https://arxiv.org/pdf/1709.10433.pdf"]}, {"id": "c924137ca87e8b4e1557465405744f8b639b16fc", "title": "Seeding Deep Learning using Wireless Localization", "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.10242.pdf"]}, {"id": "6cacda04a541d251e8221d70ac61fda88fb61a70", "title": "One-shot Face Recognition by Promoting Underrepresented Classes", "year": "2017", "pdf": ["https://arxiv.org/pdf/1707.05574.pdf"]}, {"id": "d00787e215bd74d32d80a6c115c4789214da5edb", "title": "Faster and Lighter Online Sparse Dictionary Learning Project report", "year": "", "pdf": ["https://pdfs.semanticscholar.org/d007/87e215bd74d32d80a6c115c4789214da5edb.pdf"]}, {"id": "934f69d038721540c96093cae634573b89d7d714", "title": "DeSTNet : Densely Fused Spatial Transformer Networks 1", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/934f/69d038721540c96093cae634573b89d7d714.pdf"]}, {"id": "1b805f8cbcffe7f5e2cc7af86b5649330b15298d", "title": "Heterogeneous Face Recognition Using Domain Specific Units", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/1b80/5f8cbcffe7f5e2cc7af86b5649330b15298d.pdf"]}, {"id": "9ba4a5e0b7bf6e26563d294f1f3de44d95b7f682", "title": "To Frontalize or Not to Frontalize: Do We Really Need Elaborate Pre-processing to Improve Face Recognition?", "year": "2018", "pdf": ["http://docs.wixstatic.com/ugd/445e27_b7f15ceb15d34e45836f98d9eeba9a78.pdf", "https://arxiv.org/pdf/1610.04823v1.pdf"]}, {"id": "a52a69bf304d49fba6eac6a73c5169834c77042d", "title": "Margin Loss: Making Faces More Separable", "year": "2018", "pdf": []}, {"id": "a50fa5048c61209149de0711b5f1b1806b43da00", "title": "Deep Features for Recognizing Disguised Faces in the Wild", "year": "2018", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Bansal_Deep_Features_for_CVPR_2018_paper.pdf"]}, {"id": "9e1b0f50417867317a8cb8fe35c6b2617ad9641e", "title": "Diversity in Faces", "year": "2019", "pdf": ["https://arxiv.org/pdf/1901.10436.pdf"]}, {"id": "ca5e9973a4494c608548f639eb9a391f6235d4f0", "title": "Robust RGB-D Face Recognition Using Attribute-Aware Loss", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.09847.pdf"]}, {"id": "d2eb1079552fb736e3ba5e494543e67620832c52", "title": "DeSTNet: Densely Fused Spatial Transformer Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.04050.pdf"]}, {"id": "91e17338a12b5e570907e816bff296b13177971e", "title": "Towards open-set face recognition using hashing functions", "year": "2017", "pdf": ["http://homepages.dcc.ufmg.br/~william/papers/paper_2017_IJCB.pdf"]}, {"id": "cfb8bc66502fb5f941ecdb22aec1fdbfdb73adce", "title": "Git Loss for Deep Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.08512.pdf"]}, {"id": "31b05f65405534a696a847dd19c621b7b8588263", "title": "UMDFaces: An annotated face dataset for training deep networks", "year": "2017", "pdf": []}, {"id": "dde5125baefa1141f1ed50479a3fd67c528a965f", "title": "Synthesizing Normalized Faces from Facial Identity Features", "year": "2017", "pdf": ["https://arxiv.org/pdf/1701.04851.pdf"]}, {"id": "75249ebb85b74e8932496272f38af274fbcfd696", "title": "Face Identification in Large Galleries", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/7524/9ebb85b74e8932496272f38af274fbcfd696.pdf"]}, {"id": "9ebf00558968a7ca6e130b61e70bbe2e24706da4", "title": "Ongoing face recognition vendor test (FRVT) part 2:: identification", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/5cfe/f7aedc2651d7754911a7a5cf9ee4edbe876a.pdf"]}, {"id": "59fc69b3bc4759eef1347161e1248e886702f8f7", "title": "Final Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/59fc/69b3bc4759eef1347161e1248e886702f8f7.pdf"]}, {"id": "5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952", "title": "Learning towards Minimum Hyperspherical Energy", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.09298.pdf"]}, {"id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "title": "Level Playing Field for Million Scale Face Recognition", "year": "2017", "pdf": []}, {"id": "d818568838433a6d6831adde49a58cef05e0c89f", "title": "AgeDB: The First Manually Collected, In-the-Wild Age Database", "year": "2017", "pdf": ["http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf", "http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf", "https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf"]}, {"id": "4f0b641860d90dfa4c185670bf636149a2b2b717", "title": "Improve Cross-Domain Face Recognition with IBN-block", "year": "2018", "pdf": []}, {"id": "284d8ffb2f2d3bc9f793b82f8b7f75f2751b05d7", "title": "Disguised Faces in the Wild", "year": "2018", "pdf": ["http://iab-rubric.org/papers/2018_CVPRW_disguised-faces-wild.pdf", "http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Kushwaha_Disguised_Faces_in_CVPR_2018_paper.pdf"]}, {"id": "d31328b12eef33e7722b8e5505d0f9d9abe2ffd9", "title": "Deep Unsupervised Domain Adaptation for Face Recognition", "year": "2018", "pdf": []}, {"id": "2b2896f41480399e7182acfa9466a3f915885387", "title": "CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing", "year": "2018", "pdf": ["https://arxiv.org/pdf/1812.00408.pdf"]}, {"id": "3baa3d5325f00c7edc1f1427fcd5bdc6a420a63f", "title": "Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss", "year": "2018", "pdf": ["https://arxiv.org/pdf/1707.07923.pdf"]}, {"id": "8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b", "title": "Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning", "year": "2017", "pdf": ["https://arxiv.org/pdf/1711.10520.pdf"]}, {"id": "28cd46a078e8fad370b1aba34762a874374513a5", "title": "cvpaper.challenge in 2016: Futuristic Computer Vision through 1, 600 Papers Survey", "year": "2017", "pdf": ["https://arxiv.org/pdf/1707.06436.pdf"]}, {"id": "f60070d3a4d333aa1436e4c372b1feb5b316a7ba", "title": "Face Recognition via Centralized Coordinate Learning", "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.05678.pdf"]}, {"id": "726f76f11e904d7fcb12736c276a0b00eb5cde49", "title": "A Performance Comparison of Loss Functions for Deep Face Recognition", "year": "2019", "pdf": ["https://arxiv.org/pdf/1901.05903.pdf"]}, {"id": "53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f", "title": "Accelerated Training for Massive Classification via Dynamic Class Selection", "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.01687.pdf"]}, {"id": "173657da03e3249f4e47457d360ab83b3cefbe63", "title": "HKU-Face : A Large Scale Dataset for Deep Face Recognition Final Report", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/1736/57da03e3249f4e47457d360ab83b3cefbe63.pdf"]}]}
\ No newline at end of file |