GCLNet: Generalized Contrastive Learning for Weakly Supervised Temporal Action Localization
Jing Wang, Dehui Kong, Baocai Yin · IEEE Transactions on Big Data · 2025
Weakly supervised temporal action localization (WTAL) aims to precisely locate action instances in given videos by video-level classification supervision, which is partly related to action classification. Most existing localization works directly utilize feature encoders pre-trained for video classification tasks to extract video features, resulting in non-targeted features that lead to incomplete or over-complete action localization. Therefore, we propose Generalized Contrast Learning Network (GCLNet), in which two novel strategies are proposed to improve the pre-trained features. First, to address the issue of over-completeness, GCLNet introduces text information with good context independence and category separability to enrich the expression of video features, as well as proposes a novel generalized contrastive learning approach for similarity metrics, which facilitates pulling closer the features belonging to the same category while pushing farther apart those from different categories. Consequently, it enables more compact intra-class feature learning and ensures accurate action localization. Second, to tackle the problem of incomplete, we exploit the respective advantages of RGB and Flow features in scene appearance and temporal motion expression, designing a hybrid attention strategy in GCLNet to enhance each channel features mutually. This process greatly improves the features through establishing cross-channel consensus. Finally, we conduct extensive experiments on THUMOS14 and ActivityNet1.2, respectively, and the results show that our proposed GCLNet can produce more representative action localization features.