Understanding Long Videos with Multimodal Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910999220584448 |
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| author | Ranasinghe, Kanchana Li, Xiang Kahatapitiya, Kumara Ryoo, Michael S. |
| author_facet | Ranasinghe, Kanchana Li, Xiang Kahatapitiya, Kumara Ryoo, Michael S. |
| contents | Large Language Models (LLMs) have allowed recent LLM-based approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world knowledge and strong reasoning skills of underlying LLMs influence this strong performance. Surprisingly, we discover that LLM-based approaches can yield surprisingly good accuracy on long-video tasks with limited video information, sometimes even with no video specific information. Building on this, we explore injecting video-specific information into an LLM-based framework. We utilize off-the-shelf vision tools to extract three object-centric information modalities from videos, and then leverage natural language as a medium for fusing this information. Our resulting Multimodal Video Understanding (MVU) framework demonstrates state-of-the-art performance across multiple video understanding benchmarks. Strong performance also on robotics domain tasks establish its strong generality. Code: https://github.com/kahnchana/mvu |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Long Videos with Multimodal Language Models Ranasinghe, Kanchana Li, Xiang Kahatapitiya, Kumara Ryoo, Michael S. Computer Vision and Pattern Recognition Large Language Models (LLMs) have allowed recent LLM-based approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world knowledge and strong reasoning skills of underlying LLMs influence this strong performance. Surprisingly, we discover that LLM-based approaches can yield surprisingly good accuracy on long-video tasks with limited video information, sometimes even with no video specific information. Building on this, we explore injecting video-specific information into an LLM-based framework. We utilize off-the-shelf vision tools to extract three object-centric information modalities from videos, and then leverage natural language as a medium for fusing this information. Our resulting Multimodal Video Understanding (MVU) framework demonstrates state-of-the-art performance across multiple video understanding benchmarks. Strong performance also on robotics domain tasks establish its strong generality. Code: https://github.com/kahnchana/mvu |
| title | Understanding Long Videos with Multimodal Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.16998 |