ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning

2026-05-19

AI Summary

This work addresses the limited generalization of existing promptable segmentation methods on context-dependent and reasoning-intensive complex concepts. It formalizes concept segmentation as a rule-guided concept grounding task and introduces a three-tier conceptual taxonomy—comprising Concrete Instance (CI), Contextual Description (CD), and Compositional Reasoning (CR)—to characterize cognitive complexity. The authors propose Meta-GRPO, a meta-reinforcement learning mechanism that learns transferable rules from visual exemplars and integrates agent-based reasoning with a lightweight translation module to enable end-to-end generation of segmentation prompts from reasoning states, while preserving an efficient inference path for simple scenarios. Compatible with mainstream promptable segmentation backbones, the method demonstrates comprehensive coverage across all conceptual tiers on CR benchmarks spanning natural, industrial, and medical domains, significantly improving performance on complex concept segmentation without compromising baseline efficiency.

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. In this work, we formalize generalized concept segmentation through a three-level taxonomy consisting of context-independent (CI), context-dependent (CD), and context-reasoning (CR) concepts, which reveals a clear capability gap across increasing levels of cognitive complexity. To address this challenge, we propose ConceptSeg-R1, a unified framework that reformulates concept segmentation as rule-induced concept grounding. At the core of our method is Meta-GRPO, a meta-reinforcement learning mechanism that learns transferable task rules from visual demonstrations and verifies them through proxy reasoning. The inferred reasoning states are then translated into segmentation-ready concept prompts via a lightweight concept translation module, enabling deductive application to target images. A shortcut routing strategy further preserves the native efficiency of segmentation models on simple cases. To systematically evaluate generalized concept segmentation, we conduct extensive experiments across diverse CI, CD, and CR concept segmentation benchmarks spanning natural, industrial, medical and reasoning-intensive domains. Without bells and whistles, ConceptSeg-R1 achieves strong performance across the full concept hierarchy while maintaining the native capability of promptable segmentation backbones. As an initial step toward segmenting any concept, we hope ConceptSeg-R1 can serve as a practical baseline for advancing segmentation from object-level prediction toward concept-level understanding.

Problem

• concept segmentation
• generalization
• cognitive complexity
• promptable segmentation
• visual understanding

Innovation

• meta-reinforcement learning
• concept segmentation
• rule-induced grounding
• promptable segmentation
• reasoning-based vision