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of 31
pro vyhledávání: '"Peng Jianteng"'
The outbreak of COVID-19 pandemic make people wear masks more frequently than ever. Current general face recognition system suffers from serious performance degradation,when encountering occluded scenes. The potential reason is that face features are
Externí odkaz:
http://arxiv.org/abs/2311.11512
Autor:
Chen, Jiawei, Yang, Xiao, Yin, Heng, Ma, Mingzhi, Chen, Bihui, Peng, Jianteng, Guo, Yandong, Yin, Zhaoxia, Su, Hang
Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques. Despite considerable advancements in this domain, the ability of even the most state-of-the-art methods to
Externí odkaz:
http://arxiv.org/abs/2308.02116
Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While substantial efforts have been dedicated to making neural networks inva
Externí odkaz:
http://arxiv.org/abs/2307.11528
Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Although many efforts have been devoted to making neural networks invaria
Externí odkaz:
http://arxiv.org/abs/2307.10235
Deep Neural Networks (DNNs) have recently made significant progress in many fields. However, studies have shown that DNNs are vulnerable to adversarial examples, where imperceptible perturbations can greatly mislead DNNs even if the full underlying m
Externí odkaz:
http://arxiv.org/abs/2305.04436
Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in h
Externí odkaz:
http://arxiv.org/abs/2212.13038
Publikováno v:
CVPR
Large-scale face datasets usually exhibit a massive number of classes, a long-tailed distribution, and severe label noise, which undoubtedly aggravate the difficulty of training. In this paper, we propose a training strategy that treats the head data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2dbe132094856ebe18790cae725055a0
https://doi.org/10.1109/cvpr.2019.00800
https://doi.org/10.1109/cvpr.2019.00800
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