Abstract
Object Re-IDentification (ReID) aims to recognize individuals across non-overlapping camera views. While recent advances have achieved remarkable progress, most existing models are constrained to either single-domain or cross-domain scenarios, limiting their real-world applicability. Single-domain models tend to overfit to domain-specific features, whereas cross-domain models often rely on diverse normalization strategies that may inadvertently suppress identity-specific discriminative cues. To address these limitations, we propose an Attribute Prompt Composition (APC) framework, which exploits textual semantics to jointly enhance discrimination and generalization. Specifically, we design an Attribute Prompt Generator (APG) consisting of a Semantic Attribute Dictionary (SAD) and a Prompt Composition Module (PCM). SAD is an over-complete attribute dictionary to provide rich semantic descriptions, while PCM adaptively composes relevant attributes from SAD to generate discriminative attribute-aware features. In addition, motivated by the strong generalization ability of Vision-Language Models (VLM), we propose a Fast-Slow Training Strategy (FSTS) to balance ReID-specific discrimination and generalizable representation learning. Specifically, FSTS adopts a Fast Update Stream (FUS) to rapidly acquire ReID-specific discriminative knowledge and a Slow Update Stream (SUS) to retain the generalizable knowledge inherited from the pre-trained VLM. Through a mutual interaction, the framework effectively focuses on ReID-relevant features while mitigating overfitting. Extensive experiments on both conventional and Domain Generalized (DG) ReID datasets demonstrate that our framework surpasses state-of-the-art methods, exhibiting superior performances in terms of both discrimination and generalization. The source code is available at this https URL.
Abstract (translated)
对象重识别（Re-ID）旨在通过非重叠的摄像头视角来识别个人。尽管最近的研究取得了显著进展，但大多数现有的模型要么局限于单领域场景，要么局限于跨领域场景，这限制了它们在现实世界中的应用能力。单领域的模型往往过度拟合于特定域的特征，而跨领域的模型则常常依赖多样化的归一化策略，这些策略可能无意中抑制了身份特异性的鉴别性线索。 为了克服这些局限性，我们提出了一种属性提示合成（APC）框架，该框架利用文本语义共同增强区分度和泛化能力。具体而言，我们设计了一个属性提示生成器（APG），它包括一个语义属性词典（SAD）和一个提示合成模块（PCM）。SAD是一个过度完备的属性字典，可以提供丰富的语义描述，而PCM则从SAD中自适应地组合相关属性以生成具有鉴别性的、感知身份特征。此外，鉴于视觉语言模型(VLM)强大的泛化能力，我们提出了一种快速-慢速训练策略（FSTS），旨在平衡Re-ID特异的区分度和通用表示学习。具体而言，FSTS采用了一个快速更新流（FUS）来迅速获取Re-ID特定的鉴别性知识，以及一个慢速更新流（SUS）来保留预训练VLM继承的一般化知识。通过相互作用，该框架可以有效关注与Re-ID相关的特征，同时减轻过拟合现象。 在传统和领域泛化的Re-ID数据集上的广泛实验表明，我们的框架超越了现有的最先进方法，在区分度和泛化性能方面表现出卓越的效能。源代码可在提供的链接中获取（原文中的链接）。
URL
https://arxiv.org/abs/2509.18715
