Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions

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 CUHK03 for traditional ReID, +6.4 for clothes-changing ReID, +11.7 based clothes-changing ReID when using only RGB images, +24.9 real2 for our newly defined language-instructed ReID, +4.3 visible-infrared ReID, +2.6 datasets, the model, and code will be available at https://github.com/hwz-zju/Instruct-ReID.