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Human activities often occur in specific scene contexts, e.g., playingbasketball on a basketball court. Training a model using existing videodatasets thus inevitably captures and leverages such bias (instead of using theactual discriminative cues). The learned representation may not generalize wellto new action classes or different tasks. In this paper, we propose to mitigatescene bias for video representation learning. Specifically, we augment thestandard cross-entropy loss for action classification with 1) an adversarialloss for scene types and 2) a human mask confusion loss for videos where thehuman actors are masked out. These two losses encourage learningrepresentations that are unable to predict the scene types and the correctactions when there is no evidence. We validate the effectiveness of our methodby transferring our pre-trained model to three different tasks, includingaction classification, temporal localization, and spatio-temporal actiondetection. Our results show consistent improvement over the baseline modelwithout debiasing.

Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition