The primary color profile of the same identity is assumed to remain consistent in typical Person Re-identification (Person ReID) tasks. However, this assumption may be invalid in real-world situations and images hold variant color profiles, because of cross-modality cameras or identity with different clothing. To address this issue, we propose Color Space Learning (CSL) for those Cross-Color Person ReID problems. Specifically, CSL guides the model to be less color-sensitive with two modules: Image-level Color-Augmentation and Pixel-level Color-Transformation. The first module increases the color diversity of the inputs and guides the model to focus more on the non-color information. The second module projects every pixel of input images onto a new color space. In addition, we introduce a new Person ReID benchmark across RGB and Infrared modalities, NTU-Corridor, which is the first with privacy agreements from all participants. To evaluate the effectiveness and robustness of our proposed CSL, we evaluate it on several Cross-Color Person ReID benchmarks. Our method surpasses the state-of-the-art methods consistently. The code and benchmark are available at: https://github.com/niejiahao1998/CSL

翻译：在典型行人重识别任务中，同一身份的主颜色轮廓通常被认为保持一致。然而，由于跨模态相机或身份着装变更等实际场景，这一假设在现实场景中可能失效，图像往往呈现不同的颜色分布。针对该问题，本文提出面向跨颜色行人重识别的颜色空间学习（CSL）方法。具体而言，CSL通过两个模块引导模型降低对颜色的敏感度：图像级颜色增强模块和像素级颜色变换模块。前者增加输入图像的颜色多样性，引导模型更关注非颜色信息；后者将输入图像的每个像素投影至新的颜色空间。此外，我们引入一个跨RGB与红外模态的新型行人重识别基准数据集NTU-Corridor，这是首个获得所有参与者隐私协议的数据集。为评估所提CSL的有效性与鲁棒性，我们在多个跨颜色行人重识别基准上进行了评测。本方法持续超越现有最优方法。代码与基准数据集已开源：https://github.com/niejiahao1998/CSL