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About this paper
Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, Ziyi Yang and 5 more
A framework called VidIL enables few-shot video-to-text generation by converting video content into a hierarchical textual representation that large, frozen language models then process, achieving competitive performance across captioning, QA, and event prediction without video-specific pretraining.
Key insights
- Rich visual and temporal video information can be effectively translated into a unified textual representation, bridging the modality gap for powerful, pre-trained language models.
- Leveraging existing image-language models for visual perception and large language models for reasoning enables robust few-shot video-to-text generation without video-specific pretraining or finetuning.
- Explicit temporal markers within prompts are crucial for guiding language models to understand and incorporate sequential dynamics in video content, moving beyond static interpretations.
Summary by alphaXiv.
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