ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning
The paper introduces ConceptSeg-R1, a framework for segmenting concepts using meta-reinforcement learning. It addresses the challenges of concept-level understanding in visual perception by formalizing a taxonomy of concepts. The proposed method demonstrates strong performance across various segmentation benchmarks while maintaining efficiency.
- ▪ConceptSeg-R1 reformulates concept segmentation as rule-induced concept grounding.
- ▪The framework utilizes a meta-reinforcement learning mechanism called Meta-GRPO to learn transferable task rules.
- ▪Extensive experiments show strong performance across context-independent, context-dependent, and context-reasoning concept segmentation benchmarks.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20385 (cs) [Submitted on 19 May 2026] Title:ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Authors:Yuan Zhao, Youwei Pang, Jiaming Zuo, Wei Ji, Kailai Zhou, Bin Fan, Yunkang Cao, Lihe Zhang, Xiaofeng Liu, Huchuan Lu, Weisi Lin, Dacheng Tao, Xiaoqi Zhao View a PDF of the paper titled ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning, by Yuan Zhao and 11 other authors View PDF HTML (experimental) Abstract:Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the notion of a concept remains under-specified, making it unclear whether current methods truly generalize beyond category recognition.
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