Cloth-changing person re-identification (CC-ReID), which aims to match person identities under clothing changes, is a new rising research topic in recent years. However, typical biometrics-based CC-ReID methods often require cumbersome pose or body part estimators to learn cloth-irrelevant features from human biometric traits, which comes with high computational costs. Besides, the performance is significantly limited due to the resolution degradation of surveillance images. To address the above limitations, we propose an effective Identity-Sensitive Knowledge Propagation framework (DeSKPro) for CC-ReID. Specifically, a Cloth-irrelevant Spatial Attention module is introduced to eliminate the distraction of clothing appearance by acquiring knowledge from the human parsing module. To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network. After training, the extra computations for human parsing or face restoration are no longer required. Extensive experiments show that our framework outperforms state-of-the-art methods by a large margin. Our code is available at https://github.com/KimbingNg/DeskPro.
翻译:换衣服者重新身份(CC-ReID)旨在与服装变化下的人的身份相匹配,这是近年来一个新的研究课题,不过,典型的生物鉴别方法往往要求复杂的表面或身体部分估计者学习人类生物鉴别特征中与布有关的特征,而这种特征的计算成本很高。此外,由于监视图像的分辨率降解,其性能明显有限。为了解决上述局限性,我们提议为CC-ReID建立一个有效的身份敏感知识促进框架(DeSKPro ) 。具体地说,引入一个与衣服有关的空间关注模块,通过从人类对称模块获取知识来消除服装外观的分散。为减轻分辨率退化问题和从人类脸上挖掘对身份敏感的线索,我们提议利用先前的面部知识恢复缺失的面部细节,然后将其传播到一个较小的网络。经过培训后,不再需要为人类对面部或面部恢复进行额外的计算。广泛的实验显示,我们的框架在大边缘比状态上超越了艺术状态的方法。我们的代码可在 http://Demb/ProNbsk.