Rethinking the Faster R-CNN Architecture for Temporal Action Localization

Yu-Wei Chao, Sudheendra Vijayanarasimhan, Bryan Seybold, David A. Ross, Jia Deng, Rahul Sukthankar; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 1130-1139

Abstract


We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key shortcomings of existing approaches: (1) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations; (2) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields; and (3) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important. We achieve state-of-the-art performance for both action proposal and localization on THUMOS'14 detection benchmark and competitive performance on ActivityNet challenge.

Related Material


[pdf] [Supp] [arXiv]
[bibtex]
@InProceedings{Chao_2018_CVPR,
author = {Chao, Yu-Wei and Vijayanarasimhan, Sudheendra and Seybold, Bryan and Ross, David A. and Deng, Jia and Sukthankar, Rahul},
title = {Rethinking the Faster R-CNN Architecture for Temporal Action Localization},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}