Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913811287506944 |
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| author | He, Weizhen Deng, Yiheng Yan, Yunfeng Zhu, Feng Wang, Yizhou Bai, Lei Xie, Qingsong Qi, Donglian Ouyang, Wanli Tang, Shixiang |
| author_facet | He, Weizhen Deng, Yiheng Yan, Yunfeng Zhu, Feng Wang, Yizhou Bai, Lei Xie, Qingsong Qi, Donglian Ouyang, Wanli Tang, Shixiang |
| 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 novel instruct-ReID task that requires the model to retrieve images according to the given image or language instructions. Instruct-ReID is the first exploration of a general ReID setting, where existing 6 ReID tasks can be viewed as special cases by assigning different instructions. To facilitate research in this new instruct-ReID task, we propose a large-scale OmniReID++ benchmark equipped with diverse data and comprehensive evaluation methods e.g., task specific and task-free evaluation settings. In the task-specific evaluation setting, gallery sets are categorized according to specific ReID tasks. We propose a novel baseline model, IRM, with an adaptive triplet loss to handle various retrieval tasks within a unified framework. For task-free evaluation setting, where target person images are retrieved from task-agnostic gallery sets, we further propose a new method called IRM++ with novel memory bank-assisted learning. Extensive evaluations of IRM and IRM++ on OmniReID++ benchmark demonstrate the superiority of our proposed methods, achieving state-of-the-art performance on 10 test sets. The datasets, the model, and the code will be available at https://github.com/hwz-zju/Instruct-ReID |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17790 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification He, Weizhen Deng, Yiheng Yan, Yunfeng Zhu, Feng Wang, Yizhou Bai, Lei Xie, Qingsong Qi, Donglian Ouyang, Wanli Tang, Shixiang 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 novel instruct-ReID task that requires the model to retrieve images according to the given image or language instructions. Instruct-ReID is the first exploration of a general ReID setting, where existing 6 ReID tasks can be viewed as special cases by assigning different instructions. To facilitate research in this new instruct-ReID task, we propose a large-scale OmniReID++ benchmark equipped with diverse data and comprehensive evaluation methods e.g., task specific and task-free evaluation settings. In the task-specific evaluation setting, gallery sets are categorized according to specific ReID tasks. We propose a novel baseline model, IRM, with an adaptive triplet loss to handle various retrieval tasks within a unified framework. For task-free evaluation setting, where target person images are retrieved from task-agnostic gallery sets, we further propose a new method called IRM++ with novel memory bank-assisted learning. Extensive evaluations of IRM and IRM++ on OmniReID++ benchmark demonstrate the superiority of our proposed methods, achieving state-of-the-art performance on 10 test sets. The datasets, the model, and the code will be available at https://github.com/hwz-zju/Instruct-ReID |
| title | Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.17790 |