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Multi-scale self-attention-based feature enhancement for detection of targets with small image sizes

文献类型：期刊论文

作者Deng, Ying4,5; Hu, Xingliang3; Li, Bing3; Zhang, Congxuan4; Hu, Weiming1,2,3
刊名PATTERN RECOGNITION LETTERS
出版日期2023-02-01
卷号166页码:46-52
关键词Detection of targets with small image sizes Feature enhancement Multi-scale combination Self-attention
ISSN号0167-8655
DOI10.1016/j.patrec.2022.12.026
通讯作者Li, Bing(bli@nlpr.ia.ac.cn)
英文摘要In this paper, we propose a feature enhancement method based on multi-scale self-attention, mainly including a multi-scale feature combination module and a self-attention module. The multi-scale feature combination module integrates the multi-layers' features extracted from the backbone network in both the top-down and bottom-up directions. Then, the shallow and deep features are combined. The self-attention module enhances the feature representation by assigning attention weights to the features that have intrinsic connection to the features of the target. The multi-scale self-attention-based feature enhancement method improves the performance for detecting targets with small image sizes in complex scenes by mutual combination between deep and shallow features and between local and global features. The experimental results show the effectiveness of the proposed feature enhancement method. (c) 2023 Elsevier B.V. All rights reserved.
资助项目national key R&D program of china[2018AAA0102802] ; Natural Science Foundation of China[62036011] ; Natural Science Foundation of China[62192782] ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[U2033210] ; Beijing Natural Science Foundation[L223003] ; Major Projects of Guangdong Education Department for Foundation Research and Applied Research[2017KZDXM081] ; Major Projects of Guangdong Education Department for Foundation Research and Applied Research[2018KZDXM0 6 6] ; Guangdong Provincial University Innovation Team Project[2020KCXTD045]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000925102000001
出版者ELSEVIER
资助机构national key R&D program of china ; Natural Science Foundation of China ; Beijing Natural Science Foundation ; Major Projects of Guangdong Education Department for Foundation Research and Applied Research ; Guangdong Provincial University Innovation Team Project
源URL[http://ir.ia.ac.cn/handle/173211/51440]  
专题多模态人工智能系统全国重点实验室
通讯作者Li, Bing
作者单位1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Nanchang Hangkong Univ, Sch Aeronaut Mfg Engn, Nanchang 330063, Peoples R China
5.Nanjing Univ Aeronaut & Astronaut, Coll Aerosp Engn, Nanjing 210016, Peoples R China
推荐引用方式
GB/T 7714		 Deng, Ying,Hu, Xingliang,Li, Bing,et al. Multi-scale self-attention-based feature enhancement for detection of targets with small image sizes[J]. PATTERN RECOGNITION LETTERS,2023,166:46-52.
APA Deng, Ying,Hu, Xingliang,Li, Bing,Zhang, Congxuan,&Hu, Weiming.(2023).Multi-scale self-attention-based feature enhancement for detection of targets with small image sizes.PATTERN RECOGNITION LETTERS,166,46-52.
MLA Deng, Ying,et al."Multi-scale self-attention-based feature enhancement for detection of targets with small image sizes".PATTERN RECOGNITION LETTERS 166(2023):46-52.

入库方式： OAI收割

来源：自动化研究所

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