Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions
Authors: Weizhen He, Yiheng Deng, Shixiang Tang, Qihao Chen, Qingsong Xie, Yizhou Wang, Lei Bai, Feng Zhu, Rui Zhao, Wanli Ouyang, Donglian Qi, Yunfeng Yan

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 finetuning 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 are available at https://github.com/hwz-zju/Instruct-ReID.