Rethinking the Faster R-CNN Architecture for Temporal Action Localization

We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster RCNN 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 accomm...

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Bibliographic Details
Published in2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 1130 - 1139
Main Authors Chao, Yu-Wei, Vijayanarasimhan, Sudheendra, Seybold, Bryan, Ross, David A., Deng, Jia, Sukthankar, Rahul
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Computer architecture
Feature extraction
Image segmentation
Microsoft Windows
Object detection
Proposals
Two dimensional displays
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Summary:We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster RCNN 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.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00124