Adaptive super -resolution for person re-identification with low-resolution images
文献类型：期刊论文
| 作者 | Han, Ke1,4 ; Huang, Yan1; Song, Chunfeng1; Wang, Liang1,2,3; Tan, Tieniu1,3
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| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2021-06-01 |
| 卷号 | 114页码:12 |
| 关键词 | Person re-identification Super-resolution Body regions Adaptive feature integration |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2020.107682 |
| 通讯作者 | Huang, Yan(yhuang@nlpr.ia.ac.cn) |
| 英文摘要 | Person re-identification is challenging with low-resolution query and high-resolution gallery images. To address the resolution mismatch, many methods perform super-resolution (SR) on low-resolution queries with specifying a single scale factor. However, using a single SR module, whichever scale factor is speci-fied, always brings both advantages and drawbacks in recovering and identifying identity information. A larger scale factor recovers more details but produces excessive artifacts, while a smaller one is on the contrary. To exploit their complementary property for more robust recovery and identification, we pro-pose the Adaptive Person Super-Resolution (APSR) model. APSR jointly trains and fuses multiple SR mod-ules based on their generated visual contents, to fully compensate and learn the complementary identity features in an end-to-end manner. To improve the robustness to artifacts during fusion, our model fur-ther learns informative features by online dividing and integrating the generated body regions. Extensive experiments verify the effectiveness of our method with state-of-the-art performances. ? 2021 Elsevier Ltd. All rights reserved. |
| 资助项目 | National Key Research and Development Program of China[2016YFB1001000] ; Key Research Program of Frontier Sciences, CAS[ZDBS-LY-JSC032] ; Shandong Provincial Key Research and Development Program[2019JZZY010119] ; CAS-AIR |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000632383600004 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Key Research and Development Program of China ; Key Research Program of Frontier Sciences, CAS ; Shandong Provincial Key Research and Development Program ; CAS-AIR |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44148]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Huang, Yan |
| 作者单位 | 1.Univ Chinese Acad Sci UCAS, Ctr Res Intelligent Percept & Comp CRIPAC, Natl Lab Pattern Recognit NLPR, Inst Automat,Chinese Acad Sci CASIA, Beijing, Peoples R China 2.Chinese Acad Sci, Artificial Intelligence Res CAS AIR, Beijing, Peoples R China 3.Ctr Excellence Brain Sci & Intelligence Technol C, Beijing, Peoples R China 4.Univ Chinese Acad Sci UCAS, Sch Future Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Ke,Huang, Yan,Song, Chunfeng,et al. Adaptive super -resolution for person re-identification with low-resolution images[J]. PATTERN RECOGNITION,2021,114:12. |
| APA | Han, Ke,Huang, Yan,Song, Chunfeng,Wang, Liang,&Tan, Tieniu.(2021).Adaptive super -resolution for person re-identification with low-resolution images.PATTERN RECOGNITION,114,12. |
| MLA | Han, Ke,et al."Adaptive super -resolution for person re-identification with low-resolution images".PATTERN RECOGNITION 114(2021):12. |
入库方式： OAI收割
来源：自动化研究所
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