Pyramid scene parsing network
Proceedings of the IEEE conference on computer vision and …, 2017•openaccess.thecvf.com
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this
paper, we exploit the capability of global context information by different-region-based
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-of-the-art …
paper, we exploit the capability of global context information by different-region-based
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-of-the-art …
Abstract
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based 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-of-the-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.
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