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Noise-Tolerant Learning for Audio-Visual Action Recognition. (arXiv:2205.07611v2 [cs.CV] UPDATED)
May 23, 2022, 1:12 a.m. | Haochen Han, Qinghua Zheng, Minnan Luo, Kaiyao Miao, Feng Tian, Yan Chen
cs.CV updates on arXiv.org arxiv.org
Recently, video recognition is emerging with the help of multi-modal
learning, which focuses on integrating multiple modalities to improve the
performance or robustness of a model. Although various multi-modal learning
methods have been proposed and offer remarkable recognition results, almost all
of these methods rely on high-quality manual annotations and assume that
modalities among multi-modal data provide relevant semantic information.
Unfortunately, most widely used video datasets are collected from the Internet
and inevitably contain noisy labels and noisy correspondence. To …
More from arxiv.org / cs.CV updates on arXiv.org
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