Abstract
Cloth-changing person re-identification (CC-ReID) aims to retrieve specific pedestrians in a cloth-changing scenario. Its main challenge is to disentangle the clothing-related and clothing-unrelated features. Most existing approaches force the model to learn clothing-unrelated features by changing the color of the clothes. However, due to the lack of ground truth, these methods inevitably introduce noise, which destroys the discriminative features and leads to an uncontrollable disentanglement process. In this paper, we propose a new person re-identification network called features reconstruction disentanglement ReID (FRD-ReID), which can controllably decouple the clothing-unrelated and clothing-related features. Specifically, we first introduce the human parsing mask as the ground truth of the reconstruction process. At the same time, we propose the far away attention (FAA) mechanism and the person contour attention (PCA) mechanism for clothing-unrelated features and pedestrian contour features to improve the feature reconstruction efficiency. In the testing phase, we directly discard the clothing-related features for inference,which leads to a controllable disentanglement process. We conducted extensive experiments on the PRCC, LTCC, and Vc-Clothes datasets and demonstrated that our method outperforms existing state-of-the-art methods.
Abstract (translated)
衣物更换人识别（CC-ReID）旨在从换衣场景中检索特定行人。它的主要挑战是区分相关和无关的特征。现有方法通过改变衣服的颜色来强制模型学习无关的特征。然而，由于缺乏真实数据，这些方法不可避免地引入噪声，破坏了可区分特征，导致不可控的解纠缠过程。在本文中，我们提出了一个名为特征重构解纠缠ReID（FRD-ReID）的新人物识别网络，可以控制地解耦相关和无关特征。具体来说，我们首先引入人类解析掩码作为重构过程的地面真值。同时，我们提出了用于服装无关特征和行人轮廓特征的远方注意（FAA）机制和人物轮廓注意（PCA）机制，以提高特征重构效率。在测试阶段，我们直接丢弃与推理相关的衣服特征，从而导致可控制的解纠缠过程。我们对PRCC、LTCC和Vc-Clothes数据集进行了广泛的实验，并证明了我们的方法超越了现有最先进的方法。
URL
https://arxiv.org/abs/2407.10694
