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

TL;DR
This paper introduces instruct-ReID, a versatile person re-identification task using instructions, along with a large benchmark and baseline method, demonstrating improved performance across multiple ReID scenarios without fine-tuning.
Contribution
It proposes a new general ReID task guided by instructions, a large-scale benchmark dataset, and an adaptive triplet loss, enabling multi-purpose ReID research.
Findings
Improved mAP on traditional ReID datasets.
Enhanced performance on clothes-changing ReID scenarios.
Significant gains in language-instructed ReID.
Abstract
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%,…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Face recognition and analysis · Human Pose and Action Recognition
MethodsTriplet Loss
