ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization

• URL: http://arxiv.org/abs/2112.10977v1
• Date: Tue, 21 Dec 2021 04:18:44 GMT
• Title: ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
• Authors: Zichen Yang, Jie Qin, Di Huang
• Abstract summary: Weakly-trimmed temporal action localization (WTAL) in unsupervised videos has emerged as a practical but challenging task since only video-level labels are available. Existing approaches typically leverage off-the-shelf segment-level features, which suffer from spatial incompleteness and temporal incoherence. In this paper, we tackle this problem by enhancing segment-level representations with a simple yet effective graph convolutional network.
• Score: 39.377289930528555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available. Existing approaches typically leverage off-the-shelf segment-level features, which suffer from spatial incompleteness and temporal incoherence, thus limiting their performance. In this paper, we tackle this problem from a new perspective by enhancing segment-level representations with a simple yet effective graph convolutional network, namely action complement graph network (ACGNet). It facilitates the current video segment to perceive spatial-temporal dependencies from others that potentially convey complementary clues, implicitly mitigating the negative effects caused by the two issues above. By this means, the segment-level features are more discriminative and robust to spatial-temporal variations, contributing to higher localization accuracies. More importantly, the proposed ACGNet works as a universal module that can be flexibly plugged into different WTAL frameworks, while maintaining the end-to-end training fashion. Extensive experiments are conducted on the THUMOS'14 and ActivityNet1.2 benchmarks, where the state-of-the-art results clearly demonstrate the superiority of the proposed approach.

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