Pyramid Scene Parsing Network
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Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-regionbased context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction. The proposed approach achieves state-ofthe-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields the new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.
著者Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia
會議名稱30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
會議開始日21.07.2017
會議完結日26.07.2017
會議地點Honolulu
會議國家/地區美國
會議論文集題名30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
出版年份2017
出版社IEEE
頁次6230 - 6239
國際標準書號978-1-5386-0457-1
國際標準期刊號1063-6919
語言美式英語

上次更新時間 2020-28-11 於 01:59