Collaborative Foreground, Background, and Action Modeling Network for Weakly Supervised Temporal Action Localization

In this paper, we explore the problem of Weakly supervised Temporal Action Localization (W-TAL), where the task is to localize the temporal boundaries of all action instances in an untrimmed video with only video-level supervision. The existing W-TAL methods achieve a good action localization performance by separating the discriminative action and background frames. However, there is still a large performance gap between the weakly and fully supervised methods. The main reason comes from that there are plenty of ambiguous action and background frames in addition to the discriminative action and background frames. Due to the lack of temporal annotations in W-TAL, the ambiguous background frames may be localized as foreground and the ambiguous action frames may be suppressed as background, which result in false positives and false negatives, respectively. In this paper, we introduce a novel collaborative Foreground, Background, and Action Modeling Network (FBA Net) to suppress the background (i.e., both the discriminative and ambiguous background) frames, and localize the actual action-related (i.e., both the discriminative and ambiguous action) frames as foreground, for the precise temporal action localization. We design our FBA-Net with three branches: the foreground modeling (FM) branch, the background modeling (BM) branch, and the class-specific action and background modeling (CM) branch. The CM branch learns to highlight the video frames related to C action classes, and separate the action-related frames of C action classes from the (C + 1)th background class. The collaboration between FM and CM regularizes the consistency between the FM and the C action classes of CM, which reduces the false negative rate by localizing different actual-action-related (i.e., both the discriminative and ambiguous action) frames in a video as foreground. On the other hand, the collaboration between BM and CM regularizes the consistency between the BM and the (C + 1)th background class of CM, which reduces the false positive rate by suppressing both the discriminative and ambiguous background frames. Furthermore, the collaboration between FM and BM enforces more effective foreground background separation. To evaluate the effectiveness of our FBA-Net, we perform extensive experiments on two challenging datasets, THUMOS14 and ActivityNet1.3. The experiments show that our FBA-Net attains superior results.