CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

Ho Kei Cheng*, Jihoon Chung*, Yu-Wing Tai, Chi-Keung Tang

[arXiv] [PDF]

[Supplementary Information (Comparisons with DenseCRF included!)]

[Supplementary image results]

Introduction

CascadePSP is a deep learning model for high-resolution segmentation refinement. This repository contains our PyTorch implementation with both training and testing functionalities. We also provide the annotated UHD dataset BIG and the pretrained model.

Here are some refinement results on high-resolution images.

Network Overview

Global Step & Local Step

Global Step	Local Step

Refinement Module

Table of Contents

Running:

• Installation
• Training
• Testing on Semantic Segmentation
• Testing on Scene Parsing

Downloads:

• Pretrained Models
• BIG Dataset and Relabeled PASCAL VOC 2012

More Results

Refining the masks of Human 3.6M

Image	Original Mask	Original FG	Refined Mask	Refined FG

Credit

PSPNet implementation: https://github.com/Lextal/pspnet-pytorch

SyncBN implementation: https://github.com/vacancy/Synchronized-BatchNorm-PyTorch

If you find our work useful in your research, please cite the following:

@inproceedings{CascadePSP2020,
  title={CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement},
  author={Cheng, Ho Kei and Chung, Jihoon and Tai, Yu-Wing and Tang, Chi-Keung},
  booktitle={CVPR},
  year={2020}
}