@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Iterative Self-Correction for Text-Driven Person Re-Identification with Large Vision-Language Models
%A Luo, Guijin
%A Xie, Zequn
%A Cai, Sihang
%A Wang, Chuxin
%A Zhao, Zhou
%A Tang, Yixuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F luo-etal-2026-iterative
%X 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.
%U https://aclanthology.org/2026.findings-acl.312/
%P 6292-6301
Markdown (Informal)
[Iterative Self-Correction for Text-Driven Person Re-Identification with Large Vision-Language Models](https://aclanthology.org/2026.findings-acl.312/) (Luo et al., Findings 2026)
ACL