Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization

• URL: http://arxiv.org/abs/2206.11493v1
• Date: Thu, 23 Jun 2022 06:30:08 GMT
• Title: Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization
• Authors: Kun Xia, Le Wang, Sanping Zhou, Nanning Zheng, Wei Tang
• Abstract summary: Action features and co-occurrence features often dominate the actual action content in videos. We develop a novel auxiliary task by decoupling these two types of features within a video snippet. We term our method RefactorNet, which first explicitly factorizes the action content and regularizes its co-occurrence features.
• Score: 74.74339878286935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurring ingredients which often dominate the actual action content in videos. In this paper, we explore two orthogonal but complementary aspects of a video snippet, i.e., the action features and the co-occurrence features. Especially, we develop a novel auxiliary task by decoupling these two types of features within a video snippet and recombining them to generate a new feature representation with more salient action information for accurate action localization. We term our method RefactorNet, which first explicitly factorizes the action content and regularizes its co-occurrence features, and then synthesizes a new action-dominated video representation. Extensive experimental results and ablation studies on THUMOS14 and ActivityNet v1.3 demonstrate that our new representation, combined with a simple action detector, can significantly improve the action localization performance.

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