AAformer: Auto-Aligned Transformer for Person Re-Identification
文献类型：期刊论文
| 作者 | Zhu, Kuan4 ; Guo, Haiyun1,4,7; Zhang, Shiliang3; Wang, Yaowei5; Liu, Jing1,4; Wang, Jinqiao1,2,4,6; Tang, Ming4
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2023-08-25 |
| 页码 | 11 |
| ISSN号 | 2162-237X |
| 关键词 | Auto-alignment part-level representation person re-identification (re-ID) transformer |
| DOI | 10.1109/TNNLS.2023.3301856 |
| 通讯作者 | Guo, Haiyun(haiyun.guo@nlpr.ia.ac.cn) |
| 英文摘要 | In person re-identification (re-ID), extracting part-level features from person images has been verified to be crucial to offer fine-grained information. Most of the existing CNN-based methods only locate the human parts coarsely, or rely on pretrained human parsing models and fail in locating the identifiable nonhuman parts (e.g., knapsack). In this article, we introduce an alignment scheme in transformer architecture for the first time and propose the auto-aligned transformer (AAformer) to automatically locate both the human parts and nonhuman ones at patch level. We introduce the "Part tokens (PARTs)", which are learnable vectors, to extract part features in the transformer. A PART only interacts with a local subset of patches in self-attention and learns to be the part representation. To adaptively group the image patches into different subsets, we design the auto-alignment. Auto-alignment employs a fast variant of optimal transport (OT) algorithm to online cluster the patch embeddings into several groups with the PARTs as their prototypes. AAformer integrates the part alignment into the self-attention and the output PARTs can be directly used as part features for retrieval. Extensive experiments validate the effectiveness of PARTs and the superiority of AAformer over various state-of-the-art methods. |
| WOS关键词 | NETWORK |
| 资助项目 | Key-Area Research and Development Program of Guangdong Province[2021B0101410003] ; National Natural Science Foundation of China[62276260] ; National Natural Science Foundation of China[62002356] ; National Natural Science Foundation of China[61976210] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001060547200001 |
| 资助机构 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53197]  |
| 专题 | 紫东太初大模型研究中心 |
| 通讯作者 | Guo, Haiyun |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Wuhan AI Res, Wuhan 430073, Peoples R China 3.Peking Univ, Sch Comp Sci, Natl Key Lab Multimedia Informat Proc, Beijing 100871, Peoples R China 4.Chinese Acad Sci, Inst Automat, Fdn Model Res Ctr, Beijing 100190, Peoples R China 5.Peng Cheng Lab, Shenzhen 518066, Peoples R China 6.Peng Cheng Lab, Shenzhen 518066, Peoples R China 7.Dev Res Inst Guangzhou Smart City, Guangzhou 510805, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Kuan,Guo, Haiyun,Zhang, Shiliang,et al. AAformer: Auto-Aligned Transformer for Person Re-Identification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:11. |
| APA | Zhu, Kuan.,Guo, Haiyun.,Zhang, Shiliang.,Wang, Yaowei.,Liu, Jing.,...&Tang, Ming.(2023).AAformer: Auto-Aligned Transformer for Person Re-Identification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,11. |
| MLA | Zhu, Kuan,et al."AAformer: Auto-Aligned Transformer for Person Re-Identification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):11. |
入库方式： OAI收割
来源：自动化研究所
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