CISC-R

Code for TPAMI 2022 paper, "Querying Labeled for Unlabeled: Cross-Image Semantic Consistency Guided Semi-Supervised Semantic Segmentation".

Authors: Linshan Wu, Leyuan Fang, Xingxin He, Min He, Jiayi Ma, and Zhun Zhong

Abstract

Semi-supervised semantic segmentation aims to learn a semantic segmentation model via limited labeled images and adequate unlabeled images. The key to this task is generating reliable pseudo labels for unlabeled images. Existing methods mainly focus on producing reliable pseudo labels based on the confidence scores of unlabeled images while largely ignoring the use of labeled images with accurate annotations. In this paper, we propose a Cross-Image Semantic Consistency guided Rectifying (CISC-R) approach for semi-supervised semantic segmentation, which explicitly leverages the labeled images to rectify the generated pseudo labels. Our CISC-R is inspired by the fact that images belonging to the same class have a high pixel-level correspondence. Specifically, given an unlabeled image and its initial pseudo labels, we first query a guiding labeled image that shares the same semantic information with the unlabeled image. Then, we estimate the pixel-level similarity between the unlabeled image and the queried labeled image to form a CISC map, which guides us to achieve a reliable pixel-level rectification for the pseudo labels.

Getting Started

Prepare Dataset

• Pascal: JPEGImages | SegmentationClass
• Cityscapes: leftImg8bit | gtFine
• COCO: train2017 | val2017 | masks

├── [Your Pascal Path]
    ├── JPEGImages
    └── SegmentationClass
    
├── [Your Cityscapes Path]
    ├── leftImg8bit
    └── gtFine
    
├── [Your COCO Path]
    ├── train2017
    ├── val2017
    └── masks

Pretrained Backbone:

ResNet-50 | ResNet-101 | Xception-65

├── ./pretrained
    ├── resnet50.pth
    ├── resnet101.pth
    └── xception.pth

Train and Eval

python train.py
python eval.py

Acknowledgement

We thank ST++ for part of their codes, processed datasets, data partitions, and pretrained models.

Citation ✏️ 📄

If you find this repo useful for your research, please consider citing the paper as follows:

@ARTICLE{CISC_R2022,
  author={Wu, Linshan and Fang, Leyuan and He, Xingxin and He, Min and Ma, Jiayi and Zhong, Zhun},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Querying Labeled for Unlabeled: Cross-Image Semantic Consistency Guided Semi-Supervised Semantic Segmentation}, 
  year={2023},
  volume={45},
  number={7},
  pages={8827-8844},
  doi={10.1109/TPAMI.2022.3233584}}