Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908401646174208 |
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| author | Bi, Xiaowei Xu, Zheyuan |
| author_facet | Bi, Xiaowei Xu, Zheyuan |
| contents | While multi-modal learning has advanced significantly, current approaches often create inconsistencies in representation and reasoning of different modalities. We propose UMaT, a theoretically-grounded framework that unifies visual and auditory inputs as structured text for large language models, addressing semantic alignment, temporal synchronization, and efficient sparse information retrieval. It significantly improves state-of-the-art Long Video Question Answering accuracy (up to 13.7%, and 16.9% on long videos) via redundancy minimization and structured textual representation for unified multi-modal reasoning |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_09081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment Bi, Xiaowei Xu, Zheyuan Computer Vision and Pattern Recognition Artificial Intelligence While multi-modal learning has advanced significantly, current approaches often create inconsistencies in representation and reasoning of different modalities. We propose UMaT, a theoretically-grounded framework that unifies visual and auditory inputs as structured text for large language models, addressing semantic alignment, temporal synchronization, and efficient sparse information retrieval. It significantly improves state-of-the-art Long Video Question Answering accuracy (up to 13.7%, and 16.9% on long videos) via redundancy minimization and structured textual representation for unified multi-modal reasoning |
| title | Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.09081 |