  The reasoning segmentation task, which demands a nuanced comprehension of
intricate queries to accurately pinpoint object regions, is attracting
increasing attention. However, Multi-modal Large Language Models (MLLM) often
find it difficult to accurately localize the objects described in complex
reasoning contexts. We believe that the act of reasoning segmentation should
mirror the cognitive stages of human visual search, where each step is a
progressive refinement of thought toward the final object. Thus we introduce
the Chains of Reasoning and Segmenting (CoReS) and find this top-down visual
hierarchy indeed enhances the visual search process. Specifically, we propose a
dual-chain structure that generates multi-modal, chain-like outputs to aid the
segmentation process. Furthermore, to steer the MLLM's outputs into this
intended hierarchy, we incorporate in-context inputs as guidance. Extensive
experiments demonstrate the superior performance of our CoReS, which surpasses
the state-of-the-art method by 6.5\% on the ReasonSeg dataset. Project:
https://chain-of-reasoning-and-segmentation.github.io/.
