本文提出了基于整合全局上下文信息的金字塔池化模块的PSPNet，这种全局的先验信息能够有效地在场景语义分析中获得高质量的结果。

Jul 23, 2025 · PSPNet, an acronym for Pyramid Scene Parsing Network, constitutes a profound Deep Learning model meticulously crafted for pixel-wise semantic segmentation of images.

Dec 4, 2016 · 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 …

This repository is for ' Pyramid Scene Parsing Network ', which ranked 1st place in ImageNet Scene Parsing Challenge 2016. The code is modified from Caffe version of DeepLab v2 and …

PSPNet is another semantic segmentation model along with the Unet that has been implemented into the arcgis.learn module which can be trained to classify pixels in a raster.

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 …

May 15, 2020 · This repository is a PyTorch implementation for semantic segmentation / scene parsing. The code is easy to use for training and testing on various datasets. The codebase …

Apr 16, 2025 · PSPNet achieves very high accuracy in semantic segmentation by employing a pyramid pooling approach that analyzes information at multiple resolutions, which enables …

The Pyramid Scene Parsing Network (PSPNet), introduced in 2016 by Heng Shuang Zhao and colleagues, represented a breakthrough in this area, winning the ImageNet Scene Parsing …

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 …