ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models

Person re-identification (Re-ID) is a fundamental task in computer vision that aims to match individuals across non-overlapping camera views. Although recent visionlanguage models (VLMs) have shown strong capabilities in logical reasoning and general-purpose learning, their performance in Re-ID remains suboptimal. Existing methods either struggle to perform accurate matching based on identity-relevant features or assist image-dominated branches as auxiliary semantics. In this paper, we present ChatReID, a novel framework that pioneers a text-sidedominated retrieval paradigm, enabling flexible and interactive Re-ID across diverse scenarios. To effectively integrate the reasoning power of language models into ReID pipelines, we first construct a large-scale instruction dataset containing over 8 million prompts to guide model adaptation. We then propose a hierarchical progressive tuning (HPT) strategy, consisting of three stages: person attribute understanding, fine-grained image retrieval, and multi-modal reasoning. Extensive experiments on ten widely-used benchmarks demonstrate that ChatReID consistently outperforms existing methods, achieving state-of-the-art results across all Re-ID tasks. Moreover, ChatReID exhibits strong reasoning ability by accurately extracting and integrating fine-grained identity cues in a coherent, multi-modal manner.