CorrMatch

This repository contains the official implementation of the following paper:

CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Boyuan Sun, Yuqi Yang, Le Zhang, Ming-Ming Cheng, Qibin Hou

🔥 Our paper is accepted by IEEE Computer Vision and Pattern Recognition (CVPR) 2024 !!!

Overview

CorrMatch provides a solution for mining more high-quality regions from the unlabeled images to leverage the unlabeled data more efficiently for consistency regularization. avatar

Previous approaches mostly employ complicated training strategies to leverage unlabeled data but overlook the role of correlation maps in modeling the relationships between pairs of locations. Thus, we introduce two label propagation strategies (Pixel Propagation and Region Propagation) with the help of correlation maps.

For technical details, please refer to our full paper on arXiv.

Getting Started

Installation

git clone git@github.com:BBBBchan/CorrMatch.git
cd CorrMatch
conda create -n corrmatch python=3.9
conda activate corrmatch
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install opencv-python tqdm einops pyyaml

Pretrained Backbone:

ResNet-101

mkdir pretrained

Please put the pretrained model under pretrained dictionary.

Dataset:

Pascal VOC 2012: JPEGImages | SegmentationClass
Cityscapes: leftImg8bit | gtFine

Please modify the dataset path in configuration files.The groundtruth mask ids have already been pre-processed. You may use them directly.

Your dataset path may look like:

├── [Your Pascal Path]
    ├── JPEGImages
    └── SegmentationClass

├── [Your Cityscapes Path]
    ├── leftImg8bit
    └── gtFine

Usage

Training CorrMatch

sh tools/train.sh <num_gpu> <port>

To run on different labeled data partitions or different datasets, please modify:

config, labeled_id_path, unlabeled_id_path, and save_path in train.sh.

Evaluation

sh tools/val.sh <num_gpu> <port>

To evaluate your checkpoint, please modify checkpoint_path in val.sh.

Results

Pascal VOC 2012

Labeled images are sampled from the original high-quality training set. Results are obtained by DeepLabv3+ based on ResNet-101 with training size 321(513).

Method	1/16 (92)	1/8 (183)	1/4 (366)	1/2 (732)	Full (1464)
SupOnly	45.1	55.3	64.8	69.7	73.5
ST++	65.2	71.0	74.6	77.3	79.1
PS-MT	65.8	69.6	76.6	78.4	80.0
UniMatch	75.2	77.2	78.8	79.9	81.2
CorrMatch (Ours)	76.4	78.5	79.4	80.6	81.8

Cityscapes

Results are obtained by DeepLabv3+ based on ResNet-101.

Method	1/16 (186)	1/8 (372)	1/4 (744)	1/2 (1488)
SupOnly	65.7	72.5	74.4	77.8
UniMatch	76.6	77.9	79.2	79.5
CorrMatch (Ours)	77.3	78.5	79.4	80.4

Citation

If you find our repo useful for your research, please consider citing our paper:

@article{sun2023corrmatch,
  title={CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation},
  author={Sun, Boyuan and Yang, Yuqi and Zhang, Le and Cheng, Ming-Ming and Hou, Qibin},
  journal={IEEE Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

License

This code is licensed under the Creative Commons Attribution-NonCommercial 4.0 International for non-commercial use only. Please note that any commercial use of this code requires formal permission prior to use.

Contact

For technical questions, please contact sbysbysby123[AT]gmail.com.

For commercial licensing, please contact cmm[AT]nankai.edu.cn or andrewhoux@gmail.com.

Acknowledgement

We thank UniMatch, CPS, CutMix-Seg, DeepLabv3Plus, U²PL and other excellent works (see this project) for their amazing projects!

BBBBchan / CorrMatch

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