@inproceedings{luo-etal-2026-iterative,
    title = "Iterative Self-Correction for Text-Driven Person Re-Identification with Large Vision-Language Models",
    author = "Luo, Guijin  and
      Xie, Zequn  and
      Cai, Sihang  and
      Wang, Chuxin  and
      Zhao, Zhou  and
      Tang, Yixuan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.312/",
    pages = "6292--6301",
    ISBN = "979-8-89176-395-1",
    abstract = "Person Re-Identification (ReID) has long struggled with the semantic gap between low-level visual features and high-level identity concepts. While Vision-Language Models (VLMs) offer promising semantic understanding, existing methods typically adopt a static ``one-pass'' paradigm, converting images to text once for retrieval. This approach suffers from two critical flaws: Information Bottleneck, where converting rich visuals into text causes detail loss, and Open-Loop Failure, where initial hallucinations propagate without recourse. To address this, we propose Auto-ReID, a novel framework that reformulates ReID as an iterative ``Think-and-Refine'' process. We first introduce a Hierarchical Progressive Tuning strategy to transform a generic VLM into a specialized Re-ID expert. During inference, we deploy a closed-loop architecture comprising a Reasoner for structured attribute extraction, a Hybrid Retriever that anchors dynamic semantic queries with stable visual features to prevent drift, and a Corrector that deconstructs and verifies candidates to iteratively optimize the search. Extensive experiments on ReID datasets demonstrate that our method significantly outperforms state-of-the-art approaches, particularly in complex occlusion scenarios."
}