Thinking Before Matching: A Reinforcement Reasoning Paradigm Towards General Person Re-Identification

• URL: http://arxiv.org/abs/2604.19218v1
• Date: Tue, 21 Apr 2026 08:24:07 GMT
• Title: Thinking Before Matching: A Reinforcement Reasoning Paradigm Towards General Person Re-Identification
• Abstract summary: ReID-R is proposed as a novel reasoning-driven paradigm that achieves explicit identity understanding and reasoning.<n>Experiments on multiple ReID benchmarks demonstrate that ReID-R achieves competitive identity discrimination as superior methods.
• Score: 77.51622646061314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning identity-discriminative representations with multi-scene generality has become a critical objective in person re-identification (ReID). However, mainstream perception-driven paradigms tend to identify fitting from massive annotated data rather than identity-causal cues understanding, which presents a fragile representation against multiple disruptions. In this work, ReID-R is proposed as a novel reasoning-driven paradigm that achieves explicit identity understanding and reasoning by incorporating chain-of-thought into the ReID pipeline. Specifically, ReID-R consists of a two-stage contribution: (i) Discriminative reasoning warm-up, where a model is trained in a CoT label-free manner to acquire identity-aware feature understanding; and (ii) Efficient reinforcement learning, which proposes a non-trivial sampling to construct scene-generalizable data. On this basis, ReID-R leverages high-quality reward signals to guide the model toward focusing on ID-related cues, achieving accurate reasoning and correct responses. Extensive experiments on multiple ReID benchmarks demonstrate that ReID-R achieves competitive identity discrimination as superior methods using only 14.3K non-trivial data (20.9% of the existing data scale). Furthermore, benefit from inherent reasoning, ReID-R can provide high-quality interpretation for results.

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