Human intelligence can retrieve any person according to both visual and language descriptions. However, the current computer vision community studies specific person re-identification (ReID) tasks in different scenarios separately, which limits the applications in the real world. This paper strives to resolve this problem by proposing a new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions. Our instruct-ReID is a more general ReID setting, where existing 6 ReID tasks can be viewed as special cases by designing different instructions. We propose a large-scale OmniReID benchmark and an adaptive triplet loss as a baseline method to facilitate research in this new setting. Experimental results show that the proposed multi-purpose ReID model, trained on our OmniReID benchmark without fine-tuning, can improve +0.5%, +0.6%, +7.7% mAP on Market1501, MSMT17, CUHK03 for traditional ReID, +6.4%, +7.1%, +11.2% mAP on PRCC, VC-Clothes, LTCC for clothes-changing ReID, +11.7% mAP on COCAS+ real2 for clothes template based clothes-changing ReID when using only RGB images, +24.9% mAP on COCAS+ real2 for our newly defined language-instructed ReID, +4.3% on LLCM for visible-infrared ReID, +2.6% on CUHK-PEDES for text-to-image ReID. The datasets, the model, and code will be available at https://github.com/hwz-zju/Instruct-ReID.

翻译：人类智能能够根据视觉和语言描述检索任意行人。然而，当前计算机视觉领域在各自场景下分别研究特定的行人重识别（ReID）任务，这限制了其在实际场景中的应用。本文通过提出一种新的Instruct-ReID任务来解决这一问题——该任务要求模型根据给定的图像或语言指令检索图像。我们的Instruct-ReID是一种更通用的ReID设定，现有6种ReID任务均可通过设计不同指令被视为其特例。我们提出了大规模OmniReID基准数据集和自适应三元组损失作为基线方法，以促进该新设定下的研究。实验结果表明，在OmniReID基准上训练后无需微调的多用途ReID模型，在传统ReID任务上的Market1501、MSMT17、CUHK03数据集中分别提升+0.5%、+0.6%、+7.7% mAP；在换装ReID任务上的PRCC、VC-Clothes、LTCC数据集中分别提升+6.4%、+7.1%、+11.2% mAP；在仅使用RGB图像时的基于服装模板的换装ReID任务中，在COCAS+ real2数据集上提升+11.7% mAP；在我们新定义的语言指令ReID任务中，在COCAS+ real2数据集上提升+24.9% mAP；在可见光-红外ReID任务的LLCM数据集上提升+4.3% mAP；在文图ReID任务的CUHK-PEDES数据集上提升+2.6% mAP。数据集、模型和代码将在https://github.com/hwz-zju/Instruct-ReID发布。