ML Anthology

A Closer Look at Spatiotemporal Convolutions for Action Recognition

Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, Manohar Paluri
CVPR 2018
doi:10.1109/CVPR.2018.00675 /cvpr/2018/tran2018cvpr-closer/

Abstract

In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition. In this work we empirically demonstrate the accuracy advantages of 3D CNNs over 2D CNNs within the framework of residual learning. Furthermore, we show that factorizing the 3D convolutional filters into separate spatial and temporal components yields significantly gains in accuracy. Our empirical study leads to the design of a new spatiotemporal convolutional block ``R(2+1)D'' which produces CNNs that achieve results comparable or superior to the state-of-the-art on Sports-1M, Kinetics, UCF101, and HMDB51.

PDF CVPR Semantic Scholar

Cite

Text

Tran et al. "A Closer Look at Spatiotemporal Convolutions for Action Recognition." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00675

Markdown

[Tran et al. "A Closer Look at Spatiotemporal Convolutions for Action Recognition." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/tran2018cvpr-closer/) doi:10.1109/CVPR.2018.00675

BibTeX

@inproceedings{tran2018cvpr-closer,
  title     = {{A Closer Look at Spatiotemporal Convolutions for Action Recognition}},
  author    = {Tran, Du and Wang, Heng and Torresani, Lorenzo and Ray, Jamie and LeCun, Yann and Paluri, Manohar},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00675},
  url       = {https://mlanthology.org/cvpr/2018/tran2018cvpr-closer/}
}