Web: http://arxiv.org/abs/2209.09393

Sept. 21, 2022, 1:12 a.m. | Haodong Duan, Yue Zhao, Kai Chen, Yuanjun Xiong, Dahua Lin

cs.CV updates on arXiv.org arxiv.org

Deep learning models have achieved excellent recognition results on
large-scale video benchmarks. However, they perform poorly when applied to
videos with rare scenes or objects, primarily due to the bias of existing video
datasets. We tackle this problem from two different angles: algorithm and
dataset. From the perspective of algorithms, we propose Spatial-aware
Multi-Aspect Debiasing (SMAD), which incorporates both explicit debiasing with
multi-aspect adversarial training and implicit debiasing with the spatial
actionness reweighting module, to learn a more generic representation …

algorithms arxiv benchmarks bias representation

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