Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915264168198144 |
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| author | He, Weizhen Deng, Yiheng Tang, Shixiang Chen, Qihao Xie, Qingsong Wang, Yizhou Bai, Lei Zhu, Feng Zhao, Rui Ouyang, Wanli Qi, Donglian Yan, Yunfeng |
| author_facet | He, Weizhen Deng, Yiheng Tang, Shixiang Chen, Qihao Xie, Qingsong Wang, Yizhou Bai, Lei Zhu, Feng Zhao, Rui Ouyang, Wanli Qi, Donglian Yan, Yunfeng |
| contents | 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%, +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 will be available at https://github.com/hwz-zju/Instruct-ReID. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_07520 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions He, Weizhen Deng, Yiheng Tang, Shixiang Chen, Qihao Xie, Qingsong Wang, Yizhou Bai, Lei Zhu, Feng Zhao, Rui Ouyang, Wanli Qi, Donglian Yan, Yunfeng Computer Vision and Pattern Recognition 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%, +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 will be available at https://github.com/hwz-zju/Instruct-ReID. |
| title | Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.07520 |