VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915558466781184 |
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| author | Zhang, Jinglei Guo, Yuanfan Potamias, Rolandos Alexandros Deng, Jiankang Xu, Hang Ma, Chao |
| author_facet | Zhang, Jinglei Guo, Yuanfan Potamias, Rolandos Alexandros Deng, Jiankang Xu, Hang Ma, Chao |
| contents | In recent years, video question answering based on multimodal large language models (MLLM) has garnered considerable attention, due to the benefits from the substantial advancements in LLMs. However, these models have a notable deficiency in the domains of video temporal grounding and reasoning, posing challenges to the development of effective real-world video understanding systems. Inspired by how humans use video players to interact with the progress bar for video comprehension, we introduce VTimeCoT, a simple yet effective training-free framework, designed for high-performance video grounding and reasoning. The proposed framework incorporates two novel visual tools of the progress bar: a plug-and-play progress bar integration tool and a high-efficiency highlighting tool. In addition, to address the limitations of conventional text-based chain-of-thought (CoT) approaches, we introduce a visuotemporal CoT process that integrates cross-modality reasoning across both video and text. Our approach demonstrates significant performance improvements on both Qwen2VL-7B and GPT4o baselines in tasks of video temporal grounding and reasoning-based question answering. Finally, we showcase that the proposed framework achieves a compositional and interpretable reasoning process. Project page: https://vtimecot.github.io |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_14672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning Zhang, Jinglei Guo, Yuanfan Potamias, Rolandos Alexandros Deng, Jiankang Xu, Hang Ma, Chao Computer Vision and Pattern Recognition In recent years, video question answering based on multimodal large language models (MLLM) has garnered considerable attention, due to the benefits from the substantial advancements in LLMs. However, these models have a notable deficiency in the domains of video temporal grounding and reasoning, posing challenges to the development of effective real-world video understanding systems. Inspired by how humans use video players to interact with the progress bar for video comprehension, we introduce VTimeCoT, a simple yet effective training-free framework, designed for high-performance video grounding and reasoning. The proposed framework incorporates two novel visual tools of the progress bar: a plug-and-play progress bar integration tool and a high-efficiency highlighting tool. In addition, to address the limitations of conventional text-based chain-of-thought (CoT) approaches, we introduce a visuotemporal CoT process that integrates cross-modality reasoning across both video and text. Our approach demonstrates significant performance improvements on both Qwen2VL-7B and GPT4o baselines in tasks of video temporal grounding and reasoning-based question answering. Finally, we showcase that the proposed framework achieves a compositional and interpretable reasoning process. Project page: https://vtimecot.github.io |
| title | VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.14672 |