Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization

Natural language video localization (NLVL), a crucial task in video understanding, localizes target moments in videos corresponding to a language description. The point-supervised paradigm, which requires only a single annotated frame instead of complete temporal boundaries, offers a favorable balance between performance and annotation cost. However, the absence of full annotations impedes effective video-language alignment, thereby reducing prediction accuracy. To overcome this limitation, we propose COTEL (COllaborative Temporal consistEncy Learning), a framework that enhances alignment by leveraging the synergy between saliency detection and moment localization. COTEL integrates frame-level and segment-level consistency learning branches, which are trained to reinforce each other via a cross-consistency guidance scheme. Furthermore, we introduce a hierarchical contrastive alignment loss that combines intra-video positive alignment with inter-video negative mining to achieve comprehensive video-text matching. Extensive experiments on three widely used benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. All source code will be released.