Listen to Look: Action Recognition by Previewing Audio
In the face of the video data deluge, today's expensive clip level classifiers are increasingly impractical. We propose a framework for efficient action recognition in untrimmed video that uses audio as a preview mechanism to eliminate both short term and long term visual redundancies. First, we devise an ImgAud2Vid framework that hallucinates clip level features by distilling from lighter modalities a single frame and its accompanying audio reducing short term temporal redundancy for efficient clip level recognition. Second, building on ImgAud2Vid, we further propose ImgAud Skimming, an attention based long short term memory network that iteratively selects useful moments in untrimmed videos, reducing long term temporal redundancy for efficient video level recognition. Extensive experiments on four action recognition datasets demonstrate that our method achieves the state of the art in terms of both recognition accuracy and speed.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)