PSANet: Point-wise Spatial Attention Network for Scene Parsing
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要We notice information flow in convolutional neural networks is restricted inside local neighborhood regions due to the physical design of convolutional filters, which limits the overall understanding of complex scenes. In this paper, we propose the point-wise spatial attention network (PSANet) to relax the local neighborhood constraint. Each position on the feature map is connected to all the other ones through a self-adaptively learned attention mask. Moreover, information propagation in bi-direction for scene parsing is enabled. Information at other positions can be collected to help the prediction of the current position and vice versa, information at the current position can be distributed to assist the prediction of other ones. Our proposed approach achieves top performance on various competitive scene parsing datasets, including ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating its effectiveness and generality.
出版社接受日期12.07.2018
著者Hengshuang Zhao, Yi Zhang, Shu Liu, Jianping Shi, Chen Change Loy, Dahua Lin, Jiaya Jia
會議名稱15th European Conference on Computer Vision, ECCV 2018
會議開始日08.09.2018
會議完結日14.09.2018
會議地點Munich, Germany
會議國家/地區德國
會議論文集題名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年份2018
月份9
卷號11213
出版社Springer
頁次270 - 286
國際標準書號978-303001239-7
國際標準期刊號03029743
語言美式英語

上次更新時間 2021-12-01 於 01:15