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| author | jules@lens <julescarbon@gmail.com> | 2019-03-28 16:47:43 +0100 |
|---|---|---|
| committer | jules@lens <julescarbon@gmail.com> | 2019-03-28 16:47:43 +0100 |
| commit | 6e75f7440e4d6c81bac02c005c7f9e0e9bef1d51 (patch) | |
| tree | 0587297bb011578f10521fe48f56a326fdecaeb4 /site/datasets/unknown | |
| parent | 829d4754d9269973b8bbab3837d1a579cd45239d (diff) | |
update scrape
Diffstat (limited to 'site/datasets/unknown')
25 files changed, 25 insertions, 25 deletions
diff --git a/site/datasets/unknown/casia_webface.json b/site/datasets/unknown/casia_webface.json index 310912cf..1f1e8416 100644 --- a/site/datasets/unknown/casia_webface.json +++ b/site/datasets/unknown/casia_webface.json @@ -1 +1 @@ -{"id": "853bd61bc48a431b9b1c7cab10c603830c488e39", "citations": [{"id": "0c65226edb466204189b5aec8f1033542e2c17aa", "title": "A study of CNN outside of training conditions", "year": "2017", "pdf": []}, {"id": "f49b4ab188dd090367d9f6762473879b2bba16cf", "title": "Extreme 3D Face Reconstruction: Seeing Through Occlusions", "year": "2018", "pdf": []}, {"id": "b4f3e9fc0a2b40595ae0a625d1d768a57a7c2eba", "title": "Recognizing Disguised Faces in the Wild", "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.08837.pdf"]}, {"id": "8de1c724a42d204c0050fe4c4b4e81a675d7f57c", "title": "Deep Face Recognition: A Survey", "year": "2018", "pdf": ["https://talhassner.github.io/home/projects/DeepFaceSurvey/Masietal2018deepfacesurvey.pdf"]}, {"id": "2c052a1c77a3ec2604b3deb702d77c41418c7d3e", "title": "What Is the Challenge for Deep Learning in Unconstrained Face Recognition?", "year": "2018", "pdf": []}, {"id": "2b251294bea1e49f9a646a67e6c57c9d3e1af483", "title": "Learning to generate filters for convolutional neural networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1812.01894.pdf"]}, {"id": "f20e0eefd007bc310d2a753ba526d33a8aba812c", "title": "Accurate and robust face recognition from RGB-D images with a deep learning approach", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/116e/c3a1a8225362a3e3e445df45036fae7cadc6.pdf"]}, {"id": "2e0d56794379c436b2d1be63e71a215dd67eb2ca", "title": "Improving precision and recall of face recognition in SIPP with combination of modified mean search and LSH", "year": "2017", "pdf": ["https://arxiv.org/pdf/1709.03872.pdf"]}, {"id": "8cd9475a3a1b2bcccf2034ce8f4fe691c57a4889", "title": "Noisy Face Image Sets Refining Collaborated with Discriminant Feature Space Learning", "year": "2017", "pdf": []}, {"id": "f294278e03868257bfce132b8cf189359ada915a", "title": "Boosting Face in Video Recognition via CNN Based Key Frame Extraction", "year": "2018", "pdf": ["https://www.clarkson.edu/sites/default/files/2018-03/Boosting%20Face%20in%20Video%20Recognition%20via%20CNN%20based%20Key%20Frame%20Extraction.pdf"]}, {"id": "e6b45d5a86092bbfdcd6c3c54cda3d6c3ac6b227", "title": "Pairwise Relational Networks for Face Recognition", "year": "2018", "pdf": ["https://arxiv.org/pdf/1808.04976.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": "0ab7cff2ccda7269b73ff6efd9d37e1318f7db25", "title": "Facial Coding Scheme Reference 1 Craniofacial Distances", "year": "2019", "pdf": []}, {"id": "0aebe97a92f590bdf21cdadfddec8061c682cdb2", "title": "Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification", "year": "2018", "pdf": []}, {"id": "0cb2dd5f178e3a297a0c33068961018659d0f443", "title": "IARPA Janus Benchmark-B Face Dataset", "year": "2017", "pdf": []}, {"id": "128d99fbe91b0ee593d70f3e78fe582aaa9f8ded", "title": "Residual Encoder Decoder Network and Adaptive Prior for Face Parsing", "year": "2018", "pdf": []}, {"id": "d44a93027208816b9e871101693b05adab576d89", "title": "On the Capacity of Face Representation", "year": "2017", "pdf": ["https://arxiv.org/pdf/1709.10433.pdf"]}, {"id": "10e4172dd4f4a633f10762fc5d4755e61d52dc36", "title": "Learning Multifunctional Binary Codes for Both Category and Attribute Oriented Retrieval Tasks", "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_Learning_Multifunctional_Binary_CVPR_2017_paper.pdf", "http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Liu_Learning_Multifunctional_Binary_2017_CVPR_supplemental.pdf", "http://vipl.ict.ac.cn/homepage/rpwang/publications/Learning%20Multifunctional%20Binary%20Codes%20for%20Both%20Category%20and%20Attribute%20Oriented%20Retrieval%20Tasks_CVPR2017.pdf"]}, {"id": "6bb95a0f3668cd36407c85899b71c9fe44bf9573", "title": "Face attribute prediction using off-the-shelf CNN features", "year": "2016", "pdf": ["https://arxiv.org/pdf/1602.03935.pdf"]}, {"id": "014b4335d055679bc680a6ceb6f1a264d8ce8a4a", "title": "Are You Sure You Want To Do That? 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