Although Person Re-Identification has made impressive progress, difficult cases like occlusion, change of view-point, and similar clothing still bring great challenges. In order to tackle these challenges, extracting discriminative feature representation is crucial. Most of the existing methods focus on extracting ReID features from individual images separately. However, when matching two images, we propose that the ReID features of a query image should be dynamically adjusted based on the contextual information from the gallery image it matches. We call this type of ReID features conditional feature embedding. In this paper, we propose a novel ReID framework that extracts conditional feature embedding based on the aligned visual clues between image pairs, called Clue Alignment based Conditional Embedding (CACE-Net). CACE-Net applies an attention module to build a detailed correspondence graph between crucial visual clues in image pairs and uses discrepancy-based GCN to embed the obtained complex correspondence information into the conditional features. The experiments show that CACE-Net achieves state-of-the-art performance on three public datasets

中文翻译：

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用于人员重新识别的视觉线索对应图的条件特征嵌入


尽管行人重识别取得了令人瞩目的进展，但遮挡、视角改变、服装相似等困难情况仍然带来巨大挑战。为了应对这些挑战，提取有区别的特征表示至关重要。大多数现有方法专注于从单个图像中单独提取 ReID 特征。然而，当匹配两个图像时，我们建议应根据其匹配的图库图像的上下文信息动态调整查询图像的 ReID 特征。我们将这种类型的 ReID 特征称为条件特征嵌入。在本文中，我们提出了一种新颖的 ReID 框架，该框架基于图像对之间对齐的视觉线索来提取条件特征嵌入，称为基于线索对齐的条件嵌入（CACE-Net）。 CACE-Net 应用注意力模块在图像对中的关键视觉线索之间构建详细的对应图，并使用基于差异的 GCN 将获得的复杂对应信息嵌入到条件特征中。实验表明，CACE-Net 在三个公共数据集上实现了最先进的性能