# Prototypical Prompting for Text-to-image Person Re-identification

arXiv: https://arxiv.org/abs/2409.09427 · HTML: https://mlsanity.com/paper/2409.09427/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identification](https://mlsanity.com/paper/2503.09962/) (2025)
- [Multilingual Text-to-Image Person Retrieval via Bidirectional Relation Reasoning and Aligning](https://mlsanity.com/paper/2510.17685/) (2025)
- [Dual-Granularity Cross-Modal Identity Association for Weakly-Supervised Text-to-Person Image Matching](https://mlsanity.com/paper/2507.06744/) (2025)
- [Towards Robust Text-to-Image Person Retrieval: Multi-View Reformulation for Semantic Compensation](https://mlsanity.com/paper/2604.18376/) (2026)
- [Uncertainty-Aware Prototype Semantic Decoupling for Text-Based Person Search in Full Images](https://mlsanity.com/paper/2505.03567/) (2025)
- [Generative Retrieval for Unsupervised Text-Based Person Search](https://mlsanity.com/paper/2609.12965/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of Text-to-Image Person Re-identification (TIReID), which aims to find images of the same identity described by a text sentence from a pool of candidate images. Benefiting from Vision-Language Pre-training, such as CLIP (Contrastive Language-Image Pretraining), the TIReID techniques have achieved remarkable progress recently. However, most existing methods only focus on instance-level matching and ignore identity-level matching, which involves associating multiple images and texts belonging to the same person. In this paper, we propose a novel prototypical prompting framework (Propot) designed to simultaneously model instance-level and identity-level matching for TIReID. Our Propot transforms the identity-level matching problem into a prototype learning problem, aiming to learn identity-enriched prototypes. Specifically, Propot works by 'initialize, adapt, enrich, then aggregate'. We first use CLIP to generate high-quality initial prototypes. Then, we propose a domain-conditional prototypical prompting (DPP) module to adapt the prototypes to the TIReID task using task-related information. Further, we propose an instance-conditional prototypical prompting (IPP) module to update prototypes conditioned on intra-modal and inter-modal instances to ensure prototype diversity. Finally, we design an adaptive prototype aggregation module to aggregate these prototypes, generating final identity-enriched prototypes. With identity-enriched prototypes, we diffuse its rich identity information to instances through prototype-to-instance contrastive loss to facilitate identity-level matching. Extensive experiments conducted on three benchmarks demonstrate the superiority of Propot compared to existing TIReID methods.