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 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 baseline model trained on our OmniReID benchmark can improve +0.6%, +1.4%, 0.2% mAP on Market1501, CUHK03, MSMT17 for traditional ReID, +0.8%, +2.0%, +13.4% mAP on PRCC, VC-Clothes, LTCC for clothes-changing ReID, +11.7% mAP on COCAS+ real2 for clothestemplate based clothes-changing ReID when using only RGB images, +25.4% mAP on COCAS+ real2 for our newly defined language-instructed ReID. The dataset, 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何指定行人。然而，当前计算机视觉领域将特定行人重识别任务局限于不同场景下独立研究，这限制了实际应用。本文致力于通过提出新型指令引导行人重识别任务来解决该问题，该任务要求模型根据给定的图像或语言指令检索图像。我们的指令引导行人重识别是一种更通用的重识别设置，现有重识别任务可通过设计不同指令视为其特例。我们提出大规模OmniReID基准数据集和自适应三元组损失作为基线方法，以促进该新设置下的研究。实验结果表明，在OmniReID基准上训练的基线模型：传统重识别中Market1501、CUHK03、MSMT17的mAP分别提升+0.6%、+1.4%、0.2%；换装重识别中PRCC、VC-Clothes、LTCC的mAP分别提升+0.8%、+2.0%、+13.4%；仅使用RGB图像时，基于服装模板的换装重识别在COCAS+ real2上mAP提升+11.7%；我们新定义的语言指令重识别在COCAS+ real2上mAP提升+25.4%。数据集、模型和代码将在https://github.com/hwz-zju/Instruct-ReID 公开。