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

• URL: http://arxiv.org/abs/2502.19958v1
• Date: Thu, 27 Feb 2025 10:34:14 GMT
• Title: ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models
• Authors: Ke Niu, Haiyang Yu, Mengyang Zhao, Teng Fu, Siyang Yi, Wei Lu, Bin Li, Xuelin Qian, Xiangyang Xue,
• Abstract summary: Person re-identification (Re-ID) is a critical task in human-centric intelligent systems.<n>Recent studies have successfully integrated LVLMs with person Re-ID, yielding promising results.<n>We propose a novel, versatile, one-for-all person Re-ID framework, ChatReID.
• Score: 49.09606704563898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person re-identification (Re-ID) is a critical task in human-centric intelligent systems, enabling consistent identification of individuals across different camera views using multi-modal query information. Recent studies have successfully integrated LVLMs with person Re-ID, yielding promising results. However, existing LVLM-based methods face several limitations. They rely on extracting textual embeddings from fixed templates, which are used either as intermediate features for image representation or for prompt tuning in domain-specific tasks. Furthermore, they are unable to adopt the VQA inference format, significantly restricting their broader applicability. In this paper, we propose a novel, versatile, one-for-all person Re-ID framework, ChatReID. Our approach introduces a Hierarchical Progressive Tuning (HPT) strategy, which ensures fine-grained identity-level retrieval by progressively refining the model's ability to distinguish pedestrian identities. Extensive experiments demonstrate that our approach outperforms SOTA methods across ten benchmarks in four different Re-ID settings, offering enhanced flexibility and user-friendliness. ChatReID provides a scalable, practical solution for real-world person Re-ID applications, enabling effective multi-modal interaction and fine-grained identity discrimination.

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