ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning
Reasoning-centric video object segmentation is an inherently complex task: the query often refers to dynamics, causality, and temporal interactions, rather than static appearances. Yet existing solutions generally collapse these factors into simplified reasoning with latent embeddings, rendering the reasoning chain opaque and essentially intractable. We therefore adopt an explicit decomposition perspective and introduce ReVSeg, which executes reasoning as sequential decisions in the native interface of pretrained vision language models (VLMs). Rather than folding all reasoning into a single-step prediction, ReVSeg executes three explicit operations -- semantics interpretation, temporal evidence selection, and spatial grounding -- aligning pretrained capabilities. We further employ reinforcement learning to optimize the multi-step reasoning chain, enabling the model to self-refine its decision quality from outcome-driven signals. Experimental results demonstrate that ReVSeg attains state-of-the-art performances on standard video object segmentation benchmarks and yields interpretable reasoning trajectories. Project page is available at https://clementine24.github.io/ReVSeg/ .
69325b966d1060ca587a275f
Yifan Li
claimed_verified
2025-12-05T08:29:16.888Z
68aebbb80fdaa186aa530e8e
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2WI2GgcHoZzYSM-879ejO.png
Yifan Li(SII)
Tangerine24
user
Tangerine24
69325b966d1060ca587a2760
Yingda Yin
69325b966d1060ca587a2761
Lingting Zhu
69325b966d1060ca587a2762
Weikai Chen
69325b966d1060ca587a2763
Shengju Qian
69325b966d1060ca587a2764
Xin Wang
69325b966d1060ca587a2765
Yanwei Fu
ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning
Reasoning-centric video object segmentation is an inherently complex task: the query often refers to dynamics, causality, and temporal interactions, rather than static appearances. Yet existing solutions generally collapse these factors into simplified reasoning with latent embeddings, rendering the reasoning chain opaque and essentially intractable. We therefore adopt an explicit decomposition perspective and introduce ReVSeg, which executes reasoning as sequential decisions in the native interface of pretrained vision language models (VLMs). Rather than folding all reasoning into a single-step prediction, ReVSeg executes three explicit operations -- semantics interpretation, temporal evidence selection, and spatial grounding -- aligning pretrained capabilities. We further employ reinforcement learning to optimize the multi-step reasoning chain, enabling the model to self-refine its decision quality from outcome-driven signals. Experimental results demonstrate that ReVSeg attains state-of-the-art performances on standard video object segmentation benchmarks and yields interpretable reasoning trajectories. Project page is available at https://clementine24.github.io/ReVSeg/ .
ReVSeg, a reasoning-centric video object segmentation framework, uses sequential decision-making in pretrained vision language models and reinforcement learning to achieve state-of-the-art performance and interpretable reasoning.
video object segmentation dynamics causality temporal interactions latent embeddings pretrained vision language models semantics interpretation temporal evidence selection spatial grounding reinforcement learning reasoning trajectories