Identifying the key frames: An attention-aware sampling method for action recognition

Abstract

Deep learning based methods have achieved remarkable progress in action recognition. Existing works mainly focus on designing novel deep architectures to learn video representations for action recognition. Most existing methods treat sampled frames equally and average all the frame-level predictions to generate video-level predictions at the testing stage. However, within a video, discriminative actions may occur sparsely in a few frames whereas most other frames are irrelevant to the ground truth which may even lead to wrong results. As a result, we think that the strategy of selecting relevant frames would be a further important key to enhance the existing deep learning based action recognition. In this paper, we propose an attention-aware sampling method for action recognition, which aims to discard the irrelevant and misleading frames and preserve the most discriminative frames. We formulate the process of mining key frames from videos as a Markov decision process and train the attention agent through deep reinforcement learning without extra labels. The agent takes features and predictions from the baseline model as inputs and generates importance scores for all frames. Moreover, our approach is extensible, which can be applied to different existing deep learning based action recognition models. We achieve very competitive action recognition performance on two widely used action recognition datasets.