Abstract
Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The primary challenge in this field is to overcome the complex interplay between intra-class and inter-class variations and to identify features that remain unaffected by changes in appearance. Sufficient data collection for model training would significantly aid in addressing this problem. However, it is challenging to gather diverse datasets in practice. Current methods focus on implicitly learning identity information from the original image or introducing additional auxiliary models, which are largely limited by the quality of the image and the performance of the additional model. To address these issues, inspired by prompt learning, we propose a novel multiple information prompt learning (MIPL) scheme for cloth-changing person ReID, which learns identity robust features through the common prompt guidance of multiple messages. Specifically, the clothing information stripping (CIS) module is designed to decouple the clothing information from the original RGB image features to counteract the influence of clothing appearance. The Bio-guided attention (BGA) module is proposed to increase the learning intensity of the model for key information. A dual-length hybrid patch (DHP) module is employed to make the features have diverse coverage to minimize the impact of feature bias. Extensive experiments demonstrate that the proposed method outperforms all state-of-the-art methods on the LTCC, Celeb-reID, Celeb-reID-light, and CSCC datasets, achieving rank-1 scores of 74.8%, 73.3%, 66.0%, and 88.1%, respectively. When compared to AIM (CVPR23), ACID (TIP23), and SCNet (MM23), MIPL achieves rank-1 improvements of 11.3%, 13.8%, and 7.9%, respectively, on the PRCC dataset.
Abstract (translated)
换装行人重识别是一个更贴近现实世界的主题，专注于解决行人在更换服装后的人重新识别问题。这一领域的首要挑战在于克服类内和类间变化的复杂相互作用，并识别出不受外观改变影响的特征。为模型训练收集足够的数据将显著有助于解决这个问题。然而，在实践中收集多样化的数据集具有挑战性。当前的方法主要集中在从原始图像中隐式学习身份信息或引入额外的辅助模型，这些方法大多受到图像质量和附加模型性能的限制。为了应对这些问题，受提示学习启发，我们提出了一种针对换装行人重识别的新颖多信息提示学习（MIPL）方案，通过多个消息的共同提示指导来学习鲁棒的身份特征。具体而言，设计了服装信息剥离（CIS）模块，以从原始RGB图像特征中解耦服装信息，从而抵消服装外观的影响。提出了生物引导注意力（BGA）模块，增加模型对关键信息的学习强度。采用双长度混合补丁（DHP）模块使特征具有多样化的覆盖范围，以最小化特征偏差的影响。广泛的实验表明，所提出的方法在LTCC、Celeb-reID、Celeb-reID-light和CSCC数据集上优于所有最先进的方法，分别实现了74.8%、73.3%、66.0%和88.1%的rank-1分数。与AIM（CVPR23）、ACID（TIP23）和SCNet（MM23）相比，在PRCC数据集上，MIPL在rank-1得分上分别提高了11.3%、13.8%和7.9%。
URL
https://arxiv.org/abs/2411.00330
