Retrieve Anyone: A General-purpose Person Re-identification Task with Instructions
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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 +0.5 +0.2 mAP on COCAS+ real2 for clothestemplate based clothes-changing ReID when using only RGB images, +25.5 language-instructed ReID. The dataset, model, and code will be available at https://github.com/hwz-zju/Instruct-ReID.
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