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    <abstract>Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich pseudo-labels through simulated dialogues with MLLMs, producing fine-grained and diverse visual descriptions without manual supervision. MTI refines user queries at inference time through dynamic, dialogue-based reasoning, enabling the system to interpret and resolve vague, incomplete, or ambiguous descriptions—characteristics often seen in real-world search scenarios. Together, MTG and MTI form a unified and annotation-free framework that significantly improves retrieval accuracy, robustness, and usability. Extensive evaluations demonstrate that our method achieves competitive or superior results while eliminating the need for manual captions, paving the way for scalable and practical deployment of TBPS systems.</abstract>
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%0 Conference Proceedings
%T Chat-Driven Text Generation and Interaction for Person Retrieval
%A Xie, Zequn
%A Wang, Chuxin
%A Wang, Yeqiang
%A Cai, Sihang
%A Wang, Shulei
%A Jin, Tao
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F xie-etal-2025-chat
%X Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich pseudo-labels through simulated dialogues with MLLMs, producing fine-grained and diverse visual descriptions without manual supervision. MTI refines user queries at inference time through dynamic, dialogue-based reasoning, enabling the system to interpret and resolve vague, incomplete, or ambiguous descriptions—characteristics often seen in real-world search scenarios. Together, MTG and MTI form a unified and annotation-free framework that significantly improves retrieval accuracy, robustness, and usability. Extensive evaluations demonstrate that our method achieves competitive or superior results while eliminating the need for manual captions, paving the way for scalable and practical deployment of TBPS systems.
%R 10.18653/v1/2025.emnlp-main.266
%U https://aclanthology.org/2025.emnlp-main.266/
%U https://doi.org/10.18653/v1/2025.emnlp-main.266
%P 5259-5270

Markdown (Informal)

[Chat-Driven Text Generation and Interaction for Person Retrieval](https://aclanthology.org/2025.emnlp-main.266/) (Xie et al., EMNLP 2025)

• Chat-Driven Text Generation and Interaction for Person Retrieval (Xie et al., EMNLP 2025)

ACL

• Zequn Xie, Chuxin Wang, Yeqiang Wang, Sihang Cai, Shulei Wang, and Tao Jin. 2025. Chat-Driven Text Generation and Interaction for Person Retrieval. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 5259–5270, Suzhou, China. Association for Computational Linguistics.