Cross-domain day-night re-identification (ReID) is fundamentally challenged by the substantial visual appearance discrepancies between daytime and nighttime scenes. Existing fully supervised methods rely heavily on labor-intensive annotations, which are costly and exhibit limited generalization across domains. In this work, we investigate unsupervised day-night ReID and propose a novel framework that synergistically combines prompt learning and prototype-based representation learning to associate identities across domains without requiring manual labels. Our approach follows a progressive two-stage training strategy. In the first stage, we exploit the vision-language model to generate instance-specific textual prompts in an annotation-free manner. We employ an instance-level alignment mechanism to embed visual features and textual prompts into a unified semantic space, aligning unlabeled day/night images with learnable prompts via instance-aware dynamic-bias adaptation. In the second stage, we construct domain-specific prototype memory banks and introduce two complementary modules: i) an intra-domain identity association module to enhance feature discriminability within each domain, and ii) a cross-domain prototype matching module to reliably identify positive and negative prototype pairs, thereby establishing robust identity correspondences across day and night. Extensive experiments on public benchmarks validate the effectiveness of our method. Under the unsupervised setting, our framework attains Rank-1 accuracy comparable to state-of-the-art fully supervised methods.

翻译：跨域昼夜行人重识别面临的根本挑战在于日间与夜间场景间显著的视觉外观差异。现有的全监督方法严重依赖劳动密集型标注，不仅成本高昂，且跨域泛化能力有限。本文研究了无监督昼夜行人重识别问题，提出了一种新颖框架，该框架协同结合了提示学习与基于原型的表征学习，在无需人工标签的情况下关联跨域身份。我们的方法采用渐进式两阶段训练策略。第一阶段，利用视觉-语言模型以无标注方式生成实例特定的文本提示。我们采用实例级对齐机制，将视觉特征与文本提示嵌入统一语义空间，通过实例感知的动态偏置自适应将未标注的日/夜图像与可学习提示对齐。第二阶段，构建域特定的原型记忆库，并引入两个互补模块：(i) 域内身份关联模块，用于增强各域内特征判别性；(ii) 跨域原型匹配模块，用于可靠识别正负原型对，从而建立日/夜间稳健的身份对应关系。在公开基准上的大量实验验证了方法的有效性。在无监督设置下，本框架的Rank-1准确率可媲美最先进的全监督方法。