STM: SpatioTemporal and Motion Encoding for Action Recognition

Boyuan Jiang, MengMeng Wang, Weihao Gan, Wei Wu, Junjie Yan; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2000-2009

Abstract


Spatiotemporal and motion features are two complementary and crucial information for video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn spatiotemporal features and another flow stream to learn motion features. In this work, we aim to efficiently encode these two features in a unified 2D framework. To this end, we first propose a STM block, which contains a Channel-wise SpatioTemporal Module (CSTM) to present the spatiotemporal features and a Channel-wise Motion Module (CMM) to efficiently encode motion features. We then replace original residual blocks in the ResNet architecture with STM blcoks to form a simple yet effective STM network by introducing very limited extra computation cost. Extensive experiments demonstrate that the proposed STM network outperforms the state-of-the-art methods on both temporal-related datasets (i.e., Something-Something v1 & v2 and Jester) and scene-related datasets (i.e., Kinetics-400, UCF-101, and HMDB-51) with the help of encoding spatiotemporal and motion features together.

Related Material


[pdf]
[bibtex]
@InProceedings{Jiang_2019_ICCV,
author = {Jiang, Boyuan and Wang, MengMeng and Gan, Weihao and Wu, Wei and Yan, Junjie},
title = {STM: SpatioTemporal and Motion Encoding for Action Recognition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}