Prototype Matters: Modality-unified Prototype Self-distillation for Unsupervised Visible-infrared Person Re-identification

2026-09-10

AI Summary

本文提出一种新的跨模态学习框架，通过统一模态原型对比和原型引导的自蒸馏来优化模内和模间相似关系，以解决无监督可见-红外行人重识别中的可靠关联估计问题。

Abstract

Estimating reliable cross-modality association is crucial to unsupervised visible-infrared person re-ID. While optimal transport is shown to be a practical solution for cross-modality association, it suffers from the rigidness of hard label assignment without considering the impact of cluster noise. Moreover, enforcing only cross-modality contrast is also suboptimal, as it fails to jointly optimize the similarity relation within and across modality. In this paper, we propose a novel framework for cross-modality learning by well exploitation of prototypes: First, instead of contrasting with cross-modality prototypes, we show that modality-unified prototypical contrast facilitates better modality invariance by jointly and simultaneously optimizing similarity relation within and across-modality. Taking self-prototype as a steady teacher, we further refine the instance-prototype online relation through prototype-guided self-distillation. The two components are optimized in a unified framework, leading to a simple yet effective model. On standard VI-ReID benchmarks, we perform extensive comparison and analysis, validating the effectiveness of our proposed method. Code is available at: https://github.com/Terminator8758/PoSeD.

Problem

• unsupervised visible-infrared person re-ID
• cross-modality association
• optimal transport
• cluster noise
• modality invariance

Innovation

• modality-unified prototypical contrast
• cross-modality learning
• prototype-guided self-distillation