VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset

• URL: http://arxiv.org/abs/2305.18500v2
• Date: Sat, 7 Oct 2023 12:58:26 GMT
• Title: VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
• Authors: Sihan Chen, Handong Li, Qunbo Wang, Zijia Zhao, Mingzhen Sun, Xinxin Zhu, Jing Liu
• Abstract summary: We explore an automatically generated large-scale omni-modality video caption dataset called VAST-27M. We first collect 27 million open-domain video clips and separately train a vision and an audio captioner to generate vision and audio captions. We employ an off-the-shelf Large Language Model (LLM) to integrate the generated captions, together with subtitles and instructional prompts into omni-modality captions.
• Score: 17.927825332032477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient attention. In this paper, we resort to establish connections between multi-modality video tracks, including Vision, Audio, and Subtitle, and Text by exploring an automatically generated large-scale omni-modality video caption dataset called VAST-27M. Specifically, we first collect 27 million open-domain video clips and separately train a vision and an audio captioner to generate vision and audio captions. Then, we employ an off-the-shelf Large Language Model (LLM) to integrate the generated captions, together with subtitles and instructional prompts into omni-modality captions. Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA). Extensive experiments have been conducted to demonstrate the effectiveness of our proposed VAST-27M corpus and VAST foundation model. VAST achieves 22 new state-of-the-art results on various cross-modality benchmarks. Code, model and dataset will be released at https://github.com/TXH-mercury/VAST.

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