CoT-RVS: Zero-Shot Chain-of-Thought Reasoning Segmentation for Videos

• URL: http://arxiv.org/abs/2505.18561v3
• Date: Sat, 27 Sep 2025 03:09:32 GMT
• Title: CoT-RVS: Zero-Shot Chain-of-Thought Reasoning Segmentation for Videos
• Authors: Shiu-hong Kao, Yu-Wing Tai, Chi-Keung Tang,
• Abstract summary: We propose CoT-RVS, a novel framework employing the zero-shot Chain-of-Thought (CoT) capability of MLLM to address complex challenges by temporal-semantic reasoning.<n>CoT-RVS analyzes the visible objects within a given frame that possibly match the language query (semantic), and chooses a corresponding for each object that can be observed effortlessly among all frames (temporal)<n>Our framework's training-free feature further allows its extension to process online video streams, where the CoT is used at test time to update the object of interest when a better target starts to emerge
• Score: 59.391265901911005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning Video Object Segmentation is a challenging task, aiming at generating a mask sequence from an input video given a complex and implicit text query. While existing works finetune Multimodal Large Language Models (MLLM) for the task, they still fail in video inputs given complex temporally-sensitive queries, indicating their lack of temporal and spatial integration in complex scenarios. In this paper, we propose CoT-RVS, a novel framework employing the zero-shot Chain-of-Thought (CoT) capability of MLLM to address these complex challenges by temporal-semantic reasoning: CoT-RVS analyzes the visible objects within a given frame that possibly match the language query (semantic), and chooses a corresponding keyframe for each object that can be observed effortlessly among all frames (temporal). Notably, the CoT-RVS framework is training-free and compatible with closed-source MLLMs, which can be applied to Reasoning Video Instance Segmentation. Our framework's training-free feature further allows its extension to process online video streams, where the CoT is used at test time to update the object of interest when a better target starts to emerge and becomes visible. We conduct extensive experiments on video object segmentation with explicit and implicit queries. The results show that CoT-RVS significantly outperforms previous works in both cases, qualitatively and quantitatively.

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