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Adaptively Building a Video-language Model for Video Captioning and Retrieval without Massive Video Pretraining

Published: 28 October 2024 Publication History

Abstract

Large-scale pretrained image-language models have shown remarkable performance recently. However, building a video-language model is more challenging due to the complexity of video and the difficulty of collecting high-quality data. This paper builds a video-language model in an adaptive manner, which transfers the knowledge from the image domain and can achieve state-of-the-art performance without any further massive video pretraining. The main contributions include a Visual Perception Adapter that seamlessly and efficiently adapts a pretrained image-language model to the video domain and a fine-grained contrastive learning with Inter-modal Token Alignment that bridges semantic gaps between vision, audio, and language with less data. The proposed model is evaluated on video captioning and retrieval. Experiments demonstrate that the proposed model exhibits competitive performance compared to models pretrained on millions of video-text pairs. Notably, our model's CIDEr and R@1 scores on the MSR-VTT dataset exceed the existing state-of-the-art by 6.3% and 1.3%.

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  1. Adaptively Building a Video-language Model for Video Captioning and Retrieval without Massive Video Pretraining

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      cover image ACM Conferences
      MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
      October 2024
      11719 pages
      ISBN:9798400706868
      DOI:10.1145/3664647
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      Published: 28 October 2024

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      Author Tags

      1. deep learning
      2. multimodality
      3. transfer learning
      4. video captioning
      5. video retrieval

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      • state key development program in 14th Five-Year
      • Natural Science Foundation of China
      • the Institute for Guo Qiang, Tsinghua University

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      MM '24: The 32nd ACM International Conference on Multimedia
      October 28 - November 1, 2024
      Melbourne VIC, Australia

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      MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
      Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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