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ActionVLAD: Learning spatio-temporal aggregation for action classification

Abstract · Apr 10, 2017 15:09 ·

video spatio stream temporal action aggregation streams architectures appearance cs-cv

Arxiv Abstract

  • Rohit Girdhar
  • Deva Ramanan
  • Abhinav Gupta
  • Josef Sivic
  • Bryan Russell

In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks with learnable spatio-temporal feature aggregation. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as out-performs other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.

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