Don't Judge by the Look: Towards Motion Coherent Video Representation

While we do not focus on this particular problem, our method can partially address this issue as it will also cause hue variance in the background area and help the model to rely less on the foreground bias information as well. Knowledge Distillation is proposed to train a student network to mimic the behavior of a larger teacher model Hinton et al. (2015). To avoid the extra costs of teacher network in previous methods Park et al. (2019); Ahn et al. (2019); Tian et al. (2019), researchers have developed selfdistillation approaches that allow models to transfer their own knowledge into themselvesZhu et al. (2018); Xu & Liu (2019); Yun et al. (2020b); Zhang et al. (2019). Among them, CS-KD Yun et al. (2020b) and data distortion Xu & Liu (2019) are relevant to our work as both of them construct training pairs and encourage similar predictions. However, CS-KD uses different training samples within the class to construct the training pair, and data distortion applies the same augmentation to both training samples. In contrast, our method mainly focuses on the appearance variation in videos and utilizes the same sample with different appearances to learn the invariant representations.