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diff --git a/site/datasets/unknown/umd_faces.json b/site/datasets/unknown/umd_faces.json index 4b0c9d30..b4c69c9f 100644 --- a/site/datasets/unknown/umd_faces.json +++ b/site/datasets/unknown/umd_faces.json @@ -1 +1 @@ -{"id": "31b05f65405534a696a847dd19c621b7b8588263", "citations": [{"id": "cfb8bc66502fb5f941ecdb22aec1fdbfdb73adce", "title": "Git Loss for Deep Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.08512.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": "6932baa348943507d992aba75402cfe8545a1a9b", "title": "Stacked Hourglass Network for Robust Facial Landmark Localisation", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Yang_Stacked_Hourglass_Network_CVPR_2017_paper.pdf"]}, {"id": "83447d47bb2837b831b982ebf9e63616742bfdec", "title": "An Automatic System for Unconstrained Video-Based Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1812.04058.pdf"]}, {"id": "cc47368fe303c6cbda38caf5ac0e1d1c9d7e2a52", "title": "University Classroom Attendance Based on Deep Learning", "year": "2017", "pdf": []}, {"id": "9ea37d031a8f112292c0d0f8d731b837462714e9", "title": "Face Recognition: From Traditional to Deep Learning Methods", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.00116.pdf"]}, {"id": "8de1c724a42d204c0050fe4c4b4e81a675d7f57c", "title": "Deep Face Recognition: A Survey", "year": "2018", "pdf": ["https://talhassner.github.io/home/projects/DeepFaceSurvey/Masietal2018deepfacesurvey.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"]}, {"id": "9e1b0f50417867317a8cb8fe35c6b2617ad9641e", "title": "Diversity in Faces", "year": "2019", "pdf": ["https://arxiv.org/pdf/1901.10436.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": "c50e498ede6f5216cffd0645e747ce67fae2096a", "title": "Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.09998.pdf"]}, {"id": "2d1f86e2c7ba81392c8914edbc079ac64d29b666", "title": "Deep Heterogeneous Feature Fusion for Template-Based Face Recognition", "year": "2017", "pdf": ["https://arxiv.org/pdf/1702.04471.pdf"]}, {"id": "2306b2a8fba28539306052764a77a0d0f5d1236a", "title": "Surveillance Face Recognition Challenge", "year": "2018", "pdf": ["https://arxiv.org/pdf/1804.09691.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": "7323b594d3a8508f809e276aa2d224c4e7ec5a80", "title": "An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification", "year": "2018", "pdf": ["https://arxiv.org/pdf/1808.05508.pdf"]}, {"id": "0ab7cff2ccda7269b73ff6efd9d37e1318f7db25", "title": "Facial Coding Scheme Reference 1 Craniofacial Distances", "year": "2019", "pdf": []}, {"id": "944ea33211d67663e04d0181843db634e42cb2ca", "title": "Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition.", "year": "2018", "pdf": ["https://arxiv.org/pdf/1804.01159.pdf"]}, {"id": "f15b7c317f106816bf444ac4ffb6c280cd6392c7", "title": "Deep Disguised Faces Recognition", "year": "2018", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w1/Zhang_Deep_Disguised_Faces_CVPR_2018_paper.pdf"]}, {"id": "a2e0966f303f38b58b898d388d1c83e40b605262", "title": "ECLIPSE: Ensembles of Centroids Leveraging Iteratively Processed Spatial Eclipse Clustering", "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": "57178b36c21fd7f4529ac6748614bb3374714e91", "title": "IARPA Janus Benchmark - 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