Abstract
Person re-identification (Re-ID) aims to match person images across different camera views, with occluded Re-ID addressing scenarios where pedestrians are partially visible. While pre-trained vision-language models have shown effectiveness in Re-ID tasks, they face significant challenges in occluded scenarios by focusing on holistic image semantics while neglecting fine-grained attribute information. This limitation becomes particularly evident when dealing with partially occluded pedestrians or when distinguishing between individuals with subtle appearance differences. To address this limitation, we propose Attribute-Guide ReID (AG-ReID), a novel framework that leverages pre-trained models' inherent capabilities to extract fine-grained semantic attributes without additional data or annotations. Our framework operates through a two-stage process: first generating attribute pseudo-labels that capture subtle visual characteristics, then introducing a dual-guidance mechanism that combines holistic and fine-grained attribute information to enhance image feature extraction. Extensive experiments demonstrate that AG-ReID achieves state-of-the-art results on multiple widely-used Re-ID datasets, showing significant improvements in handling occlusions and subtle attribute differences while maintaining competitive performance on standard Re-ID scenarios.
Abstract (translated)
人再识别（Re-ID）的目标是在不同摄像机视角中匹配人物图像，而遮挡下的再识别则专门处理行人部分可见的场景。尽管预训练的视觉-语言模型在人再识别任务中表现出色，但在处理遮挡场景时却面临着重大挑战，因为它们过于关注整体图像语义，忽视了细粒度属性信息的重要性。这一限制尤其明显地表现在处理部分被遮挡的行人或区分那些外表差异微妙的人物时。 为了解决这个问题，我们提出了Attribute-Guide ReID (AG-ReID)，这是一个新颖的框架，它利用预训练模型固有的能力来提取细粒度语义属性，而无需额外的数据标注。我们的框架通过一个两阶段的过程运作：首先生成能够捕捉细微视觉特征的属性伪标签，然后引入一种双重引导机制，将整体和细粒度属性信息相结合，以增强图像特征的提取。 广泛的实验表明，AG-ReID在多个广泛使用的Re-ID数据集上取得了最先进的结果，在处理遮挡及微妙属性差异方面表现出显著改进的同时，也保持了对标准Re-ID场景的竞争性能。
URL
https://arxiv.org/abs/2508.04998
