Context Driven Network with Bayes for Weakly Supervised Temporal Action Localization

Abstract

Weakly supervised temporal action localization (WTAL) aims to detect action instances from untrimmed videos. It may cause two problems, namely action incompleteness and background disturbance, due to only video-level class labels given. In this paper, we propose a context driven network with Bayes to alleviate the two problems, in which an attention module is used to predict coarse probability for each snippet, and then a Bayesian refinement module is designed to refine the coarse results by capturing the relationship between context snippets. Finally, the coarse and refined probabilities are combined as the inputs of the classifier for training. Quantitative and qualitative studies on two benchmark datasets, i.e., THUMOS'14 and ActivityNet 1.2, demonstrate that the proposed approach exceeds state-of-the-art methods.