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This repository hosts the source code of our paper: [[CVPR 2022] Cascade Transformers for End-to-End Person Search](https://arxiv.org/abs/2203.09642). In this work, we developed a novel Cascaded Occlusion-Aware Transformer (COAT) model for end-to-end person search. The COAT model outperforms **state-of-the-art** methods on the PRW benchmark dataset by a large margin and achieves state-of-the-art performance on the CUHK-SYSU dataset. # **COAT代码使用说明**
| Dataset | mAP | Top-1 | Model | ​ 这个存储库托管了论文的源代码：[[CVPR 2022] Cascade Transformers for End-to-End Person Search](https://arxiv.org/abs/2203.09642)。在这项工作中，我们开发了一种新颖的级联遮挡感知Transformer（COAT）模型，用于端到端的人物搜索。COAT模型在PRW基准数据集上以显著的优势胜过了最先进的方法，并在CUHK-SYSU数据集上取得了最先进的性能。
| --------- | ---- | ----- | ------------------------------------------------------------ |
| CUHK-SYSU | 94.2 | 94.7 | [model](https://drive.google.com/file/d/1LkEwXYaJg93yk4Kfhyk3m6j8v3i9s1B7/view?usp=sharing) | | 数据集(Datasets) | mAP | Top-1 | Model |
| PRW | 53.3 | 87.4 | [model](https://drive.google.com/file/d/1vEd_zzFN88RgxbRMG5-WfJZgD3vmP0Xg/view?usp=sharing) | | ---------------- | ---- | ----- | ------------------------------------------------------------ |
| CUHK-SYSU | 94.2 | 94.7 | [model](https://drive.google.com/file/d/1LkEwXYaJg93yk4Kfhyk3m6j8v3i9s1B7/view?usp=sharing) |
| PRW | 53.3 | 87.4 | [model](https://drive.google.com/file/d/1vEd_zzFN88RgxbRMG5-WfJZgD3vmP0Xg/view?usp=sharing) |
**Abstract**: The goal of person search is to localize a target person from a gallery set of scene images, which is extremely challenging due to large scale variations, pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search. Specifically, our three-stage cascade design focuses on detecting people at the first stage, then progressively refines the representation for person detection and re-identification simultaneously at the following stages. The occluded attention transformer at each stage applies tighter intersection over union thresholds, forcing the network to learn coarse-to-fine pose/scale invariant features. Meanwhile, we calculate the occluded attention across instances in a mini-batch to differentiate tokens from other people or the background. In this way, we simulate the effect of other objects occluding a person of interest at the token-level. Through comprehensive experiments, we demonstrate the benefits of our method by achieving state-of-the-art performance on two benchmark datasets. **Abstract**: The goal of person search is to localize a target person from a gallery set of scene images, which is extremely challenging due to large scale variations, pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search. Specifically, our three-stage cascade design focuses on detecting people at the first stage, then progressively refines the representation for person detection and re-identification simultaneously at the following stages. The occluded attention transformer at each stage applies tighter intersection over union thresholds, forcing the network to learn coarse-to-fine pose/scale invariant features. Meanwhile, we calculate the occluded attention across instances in a mini-batch to differentiate tokens from other people or the background. In this way, we simulate the effect of other objects occluding a person of interest at the token-level. Through comprehensive experiments, we demonstrate the benefits of our method by achieving state-of-the-art performance on two benchmark datasets.