Method Details

Details for method 'PSPNet'

name	PSPNet
challenge	pixel-level semantic labeling
details	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 tasks. 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 new record of mIoU score as 85.4% on PASCAL VOC 2012 and 80.2% on Cityscapes.
publication	Pyramid Scene Parsing Network Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia CVPR 2017 https://arxiv.org/abs/1612.01105
project page / code	https://hszhao.github.io/projects/pspnet
used Cityscapes data	fine annotations, coarse annotations
used external data	ImageNet
runtime	n/a
subsampling	no
submission date	November, 2016
previous submissions	1, 2, 3, 4, 5