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Triple-strip attention mechanism-based natural disaster images classification and segmentation | |
Ma, Zhihao1,3; Yuan, Mengke1,3![]() ![]() ![]() | |
发表期刊 | VISUAL COMPUTER
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ISSN | 0178-2789 |
2022-06-18 | |
卷号 | 38期号:2022页码:3163-3173 |
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. |
关键词 | Natural disaster image analysis Image segmentation Attention mechanism |
DOI | 10.1007/s00371-022-02535-w |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Software Engineering |
WOS记录号 | WOS:000812611200001 |
出版者 | SPRINGER |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 环境多维感知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49600 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Meng, Weiliang; Xu, Shibiao |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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,2022,38(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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tsam.pdf(4674KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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