VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning

• URL: http://arxiv.org/abs/2510.14672v1
• Date: Thu, 16 Oct 2025 13:29:02 GMT
• Title: VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning
• Authors: Jinglei Zhang, Yuanfan Guo, Rolandos Alexandros Potamias, Jiankang Deng, Hang Xu, Chao Ma,
• Abstract summary: We introduce VTimeCoT, a training-free framework for video temporal grounding and reasoning.<n>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.<n>Our approach demonstrates significant performance improvements on both Qwen2VL-7B and GPT4o baselines in tasks of video temporal grounding and reasoning-based question answering.
• Score: 60.42623437635971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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