ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification

Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, Bryan Russell; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 971-980

Abstract


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 outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Girdhar_2017_CVPR,
author = {Girdhar, Rohit and Ramanan, Deva and Gupta, Abhinav and Sivic, Josef and Russell, Bryan},
title = {ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}