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

doi:10.1007/s11263-026-02777-4

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

2026-04-07

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DOI：10.1007/s11263-026-02777-4

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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.

Keywords:

Temporal consistency learning

Natural language video localization

Point-supervised

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

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