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Existing video-language pre-training methods primarily focus oninstance-level alignment between video clips and captions via globalcontrastive learning but neglect rich fine-grained local information in bothvideos and text, which is of importance to downstream tasks requiring temporallocalization and semantic reasoning. A powerful model is expected to be capableof capturing region-object correspondences and recognizing scene changes in avideo clip, reflecting spatial and temporal granularity, respectively. Tostrengthen model's understanding into such fine-grained details, we propose asimple yet effective video-language modeling framework, S-ViLM, by exploitingthe intrinsic structures of these two modalities. It includes two noveldesigns, inter-clip spatial grounding and intra-clip temporal grouping, topromote learning region-object alignment and temporal-aware features,simultaneously. Comprehensive evaluations demonstrate that S-ViLM performsfavorably against existing approaches in learning more expressiverepresentations. Specifically, S-ViLM surpasses the state-of-the-art methodssubstantially on four representative downstream tasks, covering text-videoretrieval, video question answering, video action recognition, and temporalaction localization.

Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding