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Distilling Vision-Language Models on Millions of Videos

Yue Zhao · Long Zhao · Xingyi Zhou · Jialin Wu · Chun-Te Chu · Hui Miao · Florian Schroff · Hartwig Adam · Ting Liu · Boqing Gong · Philipp Krähenbühl · Liangzhe Yuan

Arch 4A-E Poster #335
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Thu 20 Jun 10:30 a.m. PDT — noon PDT


The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human-curated video-text data available. We thus resort to fine-tuning a video-language model from a strong image-language baseline with synthesized instructional data. The resulting video-language model is then used to auto-label millions of videos to generate high-quality captions. We show the adapted video-language model performs well on a wide range of video-language benchmarks. For instance, it surpasses the best prior result on open-ended NExT-QA by 2.8%. Besides, our model generates detailed descriptions for previously unseen videos, which provide better textual supervision than existing methods. Experiments show that a video-language dual-encoder model contrastively trained on these auto-generated captions is 3.8% better than the strongest baseline that also leverages vision-language models. Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%.

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