Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. A powerful model is expected to be capable of capturing region-object correspondences and recognizing scene changes in a video clip, reflecting spatial and temporal granularity respectively. To strengthen model's understanding of such fine-grained information, we propose a simple yet effective video-language modeling framework, S-ViLM, based on intrinsic structures of these two modalities. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features simultaneously. Comprehensive evaluations demonstrate that S-ViLM performs favorably against existing approaches in learning more expressive representations.Specifically, S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks, covering text-video retrieval, video question answering, video action recognition and temporal action localization.
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding
Yuanhao Xiong Long Zhao Boqing Gong Ming-Hsuan Yang Florian Schroff Ting Liu Cho-Jui Hsieh Liangzhe Yuan