Evaluating and Mitigating Static Bias of Action Representations in the Background and the Foreground

Deep neural networks for video action recognition easily learn to utilize shortcut static features, such as background and objects instead of motion features. This results in poor generalization to atypical videos such as soccer playing on concrete surfaces (instead of soccer fields). However, due to the rarity of out-of-distribution (OOD) data, quantitative evaluation of static bias remains a difficult task. In this paper, we synthesize new sets of benchmarks to evaluate static bias of action representations, including SCUB for static cues in the background, and SCUF for static cues in the foreground. Further, we propose a simple yet effective video data augmentation technique, StillMix, that automatically identifies bias-inducing video frames; unlike similar augmentation techniques, StillMix does not need to enumerate or precisely segment biased content. With extensive experiments, we quantitatively compare and analyze existing action recognition models on the created benchmarks to reveal their characteristics. We validate the effectiveness of StillMix and show that it improves TSM (Lin, Gan, and Han 2021) and Video Swin Transformer (Liu et al. 2021) by more than 10% of accuracy on SCUB for OOD action recognition.