Rethinking the Faster R-CNN Architecture for Temporal Action Localization
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.
Cite
Text
Chao et al. "Rethinking the Faster R-CNN Architecture for Temporal Action Localization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00124Markdown
[Chao et al. "Rethinking the Faster R-CNN Architecture for Temporal Action Localization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/chao2018cvpr-rethinking/) doi:10.1109/CVPR.2018.00124BibTeX
@inproceedings{chao2018cvpr-rethinking,
title = {{Rethinking the Faster R-CNN Architecture for Temporal Action Localization}},
author = {Chao, Yu-Wei and Vijayanarasimhan, Sudheendra and Seybold, Bryan and Ross, David A. and Deng, Jia and Sukthankar, Rahul},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00124},
url = {https://mlanthology.org/cvpr/2018/chao2018cvpr-rethinking/}
}