Weakly Supervised Temporal Action Detection With Temporal Dependency Learning

Weakly supervised temporal action detection aims at localizing temporal positions of action instances in untrimmed videos with only action class labels. In general, previous methods individually classify each frame based on the appearance information and the short-term motion information, and then integrate consecutive high-response action frames into entities which serve as detected action instances. However, the long-range temporal dependencies between action frames are not fully utilized, and the detection results are more likely to be trapped in the most discriminative action segments. To alleviate this issue, we propose a novel two-branch (i.e., the coarse detection branch and the refining detection branch) detection framework with learning the long-range temporal dependencies for obtaining more accurate detection results, where only action class labels are required. The coarse detection branch is used to localize the most discriminative segments of action instances based on a typical multi-instance learning paradigm under the supervision of action class labels, whereas the refining detection branch is expected to localize the less discriminative segments of action instances via learning the long-range temporal dependencies between frames based on the proposed Transformer-style architecture and learning strategies. This collaboration mechanism takes full advantage of complementary information from the provided action class labels and the natural temporal dependencies between action frames, forming a more comprehensive solution. Consequently, our method obtains more precise detection results. Expectedly, the proposed method outperforms recent weakly supervised temporal action detection methods on dataset THUMOS14 and ActivityNet measured by mAP@tIoU and AR@AN.