ACR

Official repository for CVPR 2023 paper: WSSS via Adversarial Learning of Classifier and Reconstructor by Hyeokjun Kweon and Sung-Hoon Yoon.

Prerequisite

• Tested on Ubuntu 18.04, with Python 3.8, PyTorch 1.8.2, CUDA 11.4, single gpu.
• You can create conda environment with the provided yaml file.

conda env create -f wsss_recon.yaml

• The PASCAL VOC 2012 development kit: You need to specify place VOC2012 under ./data folder.
• ImageNet-pretrained weights for resnet38d are from [resnet_38d.params] (From the google drive for our another paper, AEFT.) You need to place the weights as ./pretrained/resnet_38d.params. Note that we have reuploaded this file, as the file had been damaged.

Usage

With the following code, you can generate CAMs (seeds) to train the segmentation network. For the further refinement, refer RIB

Training

• Please specify the name of your experiment.
• Training results are saved at ./experiment/[exp_name]

python train_pl.py --name [exp_name] --exp recon_cvpr23

Evaluation for CAM

python evaluation.py --name [exp_name] --task cam --dict_dir dict

• Pretrained weight (PASCAL, seed: 59.9% mIoU) can be downloaded here.

Citation

If our code be useful for you, please consider citing our CVPR 2023 paper using the following BibTeX entry.

@inproceedings{kweon2023weakly,
  title={Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor},
  author={Kweon, Hyeokjun and Yoon, Sung-Hoon and Yoon, Kuk-Jin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11329--11339},
  year={2023}
}

You can also check our earlier works published on ICCV 2021 (OC-CSE) and ECCV 2022 (AEFT)!

we heavily borrow the work from AffinityNet repository. Thanks for the excellent codes!

## Reference
[1] J. Ahn and S. Kwak. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.