Abstract
Clothing-change person re-identification (CC Re-ID) has attracted increasing attention in recent years due to its application prospect. Most existing works struggle to adequately extract the ID-related information from the original RGB images. In this paper, we propose an Identity-aware Feature Decoupling (IFD) learning framework to mine identity-related features. Particularly, IFD exploits a dual stream architecture that consists of a main stream and an attention stream. The attention stream takes the clothing-masked images as inputs and derives the identity attention weights for effectively transferring the spatial knowledge to the main stream and highlighting the regions with abundant identity-related information. To eliminate the semantic gap between the inputs of two streams, we propose a clothing bias diminishing module specific to the main stream to regularize the features of clothing-relevant regions. Extensive experimental results demonstrate that our framework outperforms other baseline models on several widely-used CC Re-ID datasets.
Abstract (translated)
近年来，服装变化人员再识别（CC Re-ID）由于其应用前景而备受关注。现有大多数工作难以充分从原始RGB图像中提取出与身份相关的信息。在本文中，我们提出了一种基于身份感知特征解耦（IFD）的学习框架来挖掘身份相关的特征信息。特别地，IFD采用了双流架构，包括主流和注意力流。注意力流以服装遮罩后的图像为输入，并得出用于有效传输空间知识到主流以及突出富含身份相关信息区域的身份注意权重。 为了消除两个流的输入之间的语义差距，我们在主流中提出了一种专门针对服装偏差减小的模块来规范与服装相关的区域特征。大量的实验结果表明，我们的框架在几个广泛使用的CC Re-ID数据集上优于其他基线模型。
URL
https://arxiv.org/abs/2501.05851
