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  2. 自动化研究所
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  5. 三维可视计算
Triple-strip attention mechanism-based natural disaster images classification and segmentation

文献类型：期刊论文

作者Ma, Zhihao2,4; Yuan, Mengke2,4; Gu, Jiaming2,4; Meng, Weiliang2,3,4; Xu, Shibiao1; Zhang, Xiaopeng2,3,4
刊名VISUAL COMPUTER
出版日期2022-06-18
页码11
关键词Natural disaster image analysis Image segmentation Attention mechanism
ISSN号0178-2789
DOI10.1007/s00371-022-02535-w
通讯作者Meng, Weiliang(weiliang.meng@ia.ac.cn) ; Xu, Shibiao(shibiaoxu@bupt.edu.cn)
英文摘要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.
资助项目National 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]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000812611200001
出版者SPRINGER
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/49599]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Meng, Weiliang; Xu, Shibiao
作者单位1.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Zhejiang Lab, Hangzhou, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
推荐引用方式
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,2022:11.
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,11.
MLA Ma, Zhihao,et al."Triple-strip attention mechanism-based natural disaster images classification and segmentation".VISUAL COMPUTER (2022):11.

入库方式： OAI收割

来源：自动化研究所

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