Pyramid Scene Parsing Network | RDL Research Database

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. © 2017 IEEE.

Keywords

Pascal (unit)Computer scienceParsingArtificial intelligencePyramid (geometry)PoolingExploitContext (archaeology)Benchmark (surveying)Object detectionVocabularyComputer visionNatural language processingPattern recognition (psychology)Programming languageCartography

Affiliated Institutions

Related Publications

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Liang-Chieh Chen , Yukun Zhu , George Papandreou +2 more

Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale co...

2018 Lecture notes in computer science 13300 citations

Dual Attention Network for Scene Segmentation

Jun Fu , Jing Liu , Haijie Tian +4 more

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture context...

2019 2019 IEEE/CVF Conference on Computer ... 6497 citations

Is object localization for free? - Weakly-supervised learning with convolutional neural networks

Maxime Oquab , Léon Bottou , Ivan Laptev +1 more

Successful methods for visual object recognition typically rely on training datasets containing lots of richly annotated images. Detailed image annotation, e.g. by object boundi...

2015 915 citations

Conditional Random Fields as Recurrent Neural Networks

Shuai Zheng , Sadeep Jayasumana , Bernardino Romera‐Paredes +5 more

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep lear...

2015 2381 citations

Learning Deconvolution Network for Semantic Segmentation

Hyeonwoo Noh , Seunghoon Hong , Bohyung Han

We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer ne...

2015 3978 citations

Publication Info

Year: 2017
Type: article
Pages: 6230-6239
Citations: 14618
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Pyramid Scene Parsing Network

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

14618

OpenAlex

Cite This

APA Style

                            
                                    Hengshuang Zhao, 
                                
                                    Jianping Shi, 
                                
                                    Xiaojuan Qi
                                
                                et al.
                            
                            (2017). 
                            Pyramid Scene Parsing Network. 
                            
                            , 6230-6239.
                            https://doi.org/10.1109/cvpr.2017.660

Identifiers

DOI: 10.1109/cvpr.2017.660