Cross-modality online distillation for multi-view action recognition

Abstract

Recently, some multi-modality features are introduced to the multi-view action recognition methods in order to obtain a more robust performance. However, it is intuitive that not all modalities are avail- able in real applications. For example, daily scenes lack depth modal data and capture RGB sequences only. Thus comes the challenge of learning critical features from multi-modality data at train time, while still getting robust performance based on RGB sequences at test time. To address this chal- lenge, our paper presents a novel two-stage teacher-student framework. The teacher network takes advantage of multi-view geometry-and-texture features during training, while the student network is given only RGB sequences at test time. Specifically, in the first stage, Cross-modality Aggregated Transfer (CAT) network is proposed to transfer multi-view cross-modality aggregated features from the teacher network to the student network. Moreover, we design a Viewpoint-Aware Attention (VAA) module which captures discriminative information across different views to combine multi-view fea- tures effectively. In the second stage, Multi-view Features Strengthen (MFS) network with the VAA module further strengthens the global view-invariance features of the student network. Besides, both of CAT and MFS learn in an online distillation manner, so that the teacher and the student network can be trained jointly. Extensive experiments on IXMAS and Northwestern-UCLA demonstrate the effectiveness of our proposed method.