CASIA OpenIR  > 多模态人工智能系统全国重点实验室  > 三维可视计算
Triple-strip attention mechanism-based natural disaster images classification and segmentation
Ma, Zhihao1,3; Yuan, Mengke1,3; Gu, Jiaming1,3; Meng, Weiliang1,2,3; Xu, Shibiao4; Zhang, Xiaopeng1,2,3
Source PublicationVISUAL COMPUTER
ISSN0178-2789
2022-06-18
Volume38Issue:2022Pages:3163-3173
Abstract

Fast and accurate semantic analysis of natural disaster images is crucial for rational rescue plans and resource allocation. However, the scarcity of meticulously labelled datasets and the ignorance of region-of-interest scale variations of popular general-purpose methods lead to undesirable performance. In this paper, we propose a novel triple-strip attention mechanism (TSAM) to solve the generalization problem of disaster images that can be combined into general networks. Our TSAM accumulates features of three parallel-strip attentions (row strip attention, column strip attention, and channel strip attention), and the output is multiplied with original input features for further processing. Our attention mechanism can effectively overcome the defect of ignoring global features caused by the convolution and enhance the performance of the network by weighting the features from both spatial and channel aspects more comprehensively. Besides, we employ both the compression and expansion operations in the weighting operation to reduce the amount of parameters, leading to negligible computational overhead. Experiments validate that our TSAM outperforms other state-of-the-art methods on natural disaster segmentation. Due to its concise form, plug-and-play pattern, and high promotion rate, our TSAM can be combined with many existing neural networks for better performance improvement.

KeywordNatural disaster image analysis Image segmentation Attention mechanism
DOI10.1007/s00371-022-02535-w
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[U21A20515] ; National Natural Science Foundation of China[61972459] ; National Natural Science Foundation of China[62172416] ; National Natural Science Foundation of China[62102414] ; National Natural Science Foundation of China[U2003109] ; National Natural Science Foundation of China[62071157] ; National Natural Science Foundation of China[62171321] ; National Natural Science Foundation of China[62162044] ; National Natural Science Foundation of China[2021KE0AB07] ; National Natural Science Foundation of China[TC210H00L/42]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Software Engineering
WOS IDWOS:000812611200001
PublisherSPRINGER
Sub direction classification图像视频处理与分析
planning direction of the national heavy laboratory环境多维感知
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Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49600
Collection多模态人工智能系统全国重点实验室_三维可视计算
Corresponding AuthorMeng, Weiliang; Xu, Shibiao
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Zhejiang Lab, Hangzhou, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ma, Zhihao,Yuan, Mengke,Gu, Jiaming,et al. Triple-strip attention mechanism-based natural disaster images classification and segmentation[J]. VISUAL COMPUTER,2022,38(2022):3163-3173.
APA Ma, Zhihao,Yuan, Mengke,Gu, Jiaming,Meng, Weiliang,Xu, Shibiao,&Zhang, Xiaopeng.(2022).Triple-strip attention mechanism-based natural disaster images classification and segmentation.VISUAL COMPUTER,38(2022),3163-3173.
MLA Ma, Zhihao,et al."Triple-strip attention mechanism-based natural disaster images classification and segmentation".VISUAL COMPUTER 38.2022(2022):3163-3173.
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