MSR-VTT Cross-Modal Similarity Under Varying Relevant Moments per Query in Multimodal GMR Models

Abstract

Abstract: Video Moment Retrieval (VMR) aims to localize temporal segments in videos that correspond to a natural language query, but typically assumes only a single matching moment for each query. This assumption does not always hold in real-world scenarios, where queries may correspond to multiple or no moments. Thus, we formulate Generalized Moment Retrieval (GMR), a unified setting that requires retrieving the complete set of relevant moments or predicting an empty set. To enable systematic study of GMR, we introduce Soccer-GMR, a large-scale benchmark built on challenging soccer videos that reflect

Research Question

What is the effect of varying the number of relevant moments per query on the alignment between textual queries and visual moments in multimodal GMR models, as measured by cross-modal similarity scores on MSR-VTT?

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