APLNet: Attention-enhanced progressive learning network
文献类型：期刊论文
| 作者 | Zhang, Hui1 ; Kang, Danqing1; He, Haibo4; Wang, Fei-Yue1,2,3
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-01-02 |
| 卷号 | 371页码:166-176 |
| 关键词 | Object detection Progressive learning Attention enhancement |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.08.086 |
| 通讯作者 | He, Haibo(haibohe@uri.edu) |
| 英文摘要 | Single-stage detectors depend on a simple regression network to predict category scores and regress box offsets for a fixed set of default boxes directly. The regression network needs to have high generalization capability, so as to accurately model the relationship between various object shapes and default boxes. Instead of complicating the regression network to increase generalization capability, we iteratively refine the default boxes to model this relationship sequentially. In this paper, we propose an Attention-Enhanced Progressive Learning Network (APLNet), which employs multiple stages for progressive detection to improve performance of single-stage detectors. Specifically, a progressive learning module is proposed to iteratively update the feature representation space and gradually regress the default boxes, which are pushed closer to the target objects progressively. In addition, since low-level features have less semantic information about objects, we design an attention enhancement module to generate the attention map applied to inject more semantically meaningful information into the low-level features. This module is supervised by boxes-induced segmentation annotations, i.e., no extra segmentation annotations are required. The multi-task loss function is used to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets demonstrate the effectiveness of the proposed APLNet. (C) 2019 Elsevier B.V. All rights reserved. |
| WOS关键词 | OBJECT DETECTION |
| 资助项目 | Joint Foundation of Guangdong[U1811463] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000493950600015 |
| 出版者 | ELSEVIER |
| 资助机构 | Joint Foundation of Guangdong |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28870]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | He, Haibo |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China 3.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Vis, Qingdao, Shandong, Peoples R China 4.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA |
| 推荐引用方式 GB/T 7714 | Zhang, Hui,Kang, Danqing,He, Haibo,et al. APLNet: Attention-enhanced progressive learning network[J]. NEUROCOMPUTING,2020,371:166-176. |
| APA | Zhang, Hui,Kang, Danqing,He, Haibo,&Wang, Fei-Yue.(2020).APLNet: Attention-enhanced progressive learning network.NEUROCOMPUTING,371,166-176. |
| MLA | Zhang, Hui,et al."APLNet: Attention-enhanced progressive learning network".NEUROCOMPUTING 371(2020):166-176. |
入库方式： OAI收割
来源：自动化研究所
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