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Unlocking the Potential of Ordinary Classifier: Class-specific Adversarial Erasing Framework for Weakly Supervised Semantic Segmentation

This repository contains the official PyTorch implementation of the paper "Unlocking the Potential of Ordinary Classifier: Class-specific Adversarial Erasing Framework for Weakly Supervised Semantic Segmentation" paper (ICCV 2021) by Hyeokjun Kweon and Sung-Hoon Yoon.

Introduction

We have developed a framework that extract the potential of the ordinary classifier with class-specific adversarial erasing framework for weakly supervised semantic segmentation. With image-level supervision only, we achieved new state-of-the-arts both on PASCAL VOC 2012 and MS-COCO.

Citation

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

@inproceedings{kweon2021unlocking,
  title={Unlocking the potential of ordinary classifier: Class-specific adversarial erasing framework for weakly supervised semantic segmentation},
  author={Kweon, Hyeokjun and Yoon, Sung-Hoon and Kim, Hyeonseong and Park, Daehee and Yoon, Kuk-Jin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6994--7003},
  year={2021}
}

Prerequisite

Tested on Ubuntu 16.04, with Python 3.6, PyTorch 1.5.1, CUDA 10.1, both on both single and multi gpu.
You can create conda environment with the provided yaml file.

conda env create -f od_cse.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]. You need to place the weights as ./pretrained/resnet_38d.params.
PASCAL-pretrained weights for resnet38d are from [od_cam.pth]. You need to place the weights as ./pretrained/od_cam.pth.

Usage

Training

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

python train.py --name [exp_name] --model model_cse

Inference

python infer.py --name [exp_name] --model model_cse --load_epo [epoch_to_load] --vis --dict --crf --alphas 6 10 24

Evaluation for CAM result

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

Evaluation for CRF result (ex. alpha=6)

python evaluation.py --name [exp_name] --task crf --dict_dir crf/06

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