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$R^2$-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
April 2, 2024, 7:47 p.m. | Ye Liu, Jixuan He, Wanhua Li, Junsik Kim, Donglai Wei, Hanspeter Pfister, Chang Wen Chen
cs.CV updates on arXiv.org arxiv.org
Abstract: Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries. Most existing VTG models are built upon frame-wise final-layer CLIP features, aided by additional temporal backbones (e.g., SlowFast) with sophisticated temporal reasoning mechanisms. In this work, we claim that CLIP itself already shows great potential for fine-grained spatial-temporal modeling, as each layer offers distinct yet useful information under different granularity levels. Motivated …
arxiv cs.cv image image-to-video temporal transfer transfer learning type video
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