Generative Retrieval for Unsupervised Text-Based Person Search

Abstract:Text-based person search (TBPS) aims to retrieve images of a target person from a large image gallery based on a given natural language description. Most existing methods rely on supervised learning with manually annotated image-text pairs. In this paper, we explore unsupervised TBPS, with only unlabeled images. We propose GTR+, a two-stage generation-then-retrieval framework. In the generation stage, we introduce a tiered description generation framework designed to produce fine-grained and stylistically diverse textual descriptions through a three-tier sequential process. The base tier leverages an automated question-and-answer mechanism to generate basic visual attribute descriptions; the intermediate tier enhances fine-grained detail using an inter-sample contrastive mechanism; the advanced tier further enriches textual diversity via a stylized expansion mechanism. In the retrieval stage, to mitigate the impact of noisy pseudo texts, we develop an adaptive confidence-weighted retrieval learning framework. We model image-text pairs as clean or noisy using a Gaussian Mixture Model, calibrated by real-time image-text similarity and static text generation probability from the prior stage, yielding adaptive sample weights during training. Beyond that, we also contribute LargeFine-Person, a large-scale TBPS dataset with high-quality, fine-grained, and diverse textual annotations, enabling a practical and generalizable TBPS pre-training benchmark under unsupervised setting. Experiments on multiple TBPS benchmarks demonstrate the effectiveness and generalization of both GTR+ and LargeFine-Person. Code is available at: this https URL.

17 pages, 10 figures. Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2609.12965 [cs.CV]

(or arXiv:2609.12965v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.12965 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-17, 2026

https://doi.org/10.1109/TPAMI.2026.3713379 Focus to learn more DOI(s) linking to related resources

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