Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

Understand

Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes.

• To address these limitations, we propose Seg-Zero, a novel framework that demonstrates remarkable generalizability and derives explicit chain-of-thought reasoning through cognitive reinforcement.
• Seg-Zero introduces a decoupled architecture consisting of a reasoning model and a segmentation model.
• The reasoning model interprets user intentions, generates explicit reasoning chains, and produces positional prompts, which are subsequently used by the segmentation model to generate precious pixel-level masks.