From a Glance to a Boundary: Uncertainty-Aware Distillation for Glance-Supervised Video Moment Localization

Glance-supervised video moment localization, which requires only single-frame annotations within target moments, offers a promising balance between annotation efficiency and localization precision. However, existing methods suffer from a fundamental limitation: the inherent uncertainty regarding precise moment boundaries when given only glance supervision. Current approaches either assume deterministic relationships between glance annotations and boundaries or employ predefined soft-label distributions, both failing to capture the complexity of real-world boundary uncertainty. We introduce UDist, an uncertainty-aware distillation network that explicitly models boundary prediction uncertainty as learnable probability distributions. Our framework consists of two key components: 1) an Uncertainty-aware Offset Distribution (UOD) mechanism, transforming implicit boundary uncertainty into explicit probability distributions over discretized offset bins; and 2) a dual-branch distillation framework that transfers uncertainty knowledge from a teacher to a student branch through a competitive distillation process. This “certainty to uncertainty to refined certainty” paradigm converts glance annotation certainty into uncertainty representations, then distills them into precise boundary predictions. Experiments on Charades-STA, ActivityNet Captions and TACoS show UDist improves localization accuracy while maintaining computational efficiency.