Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation

Input Image	Initial CAM	Successive Maps with adversarial climbing

The implementation of Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation, Jungbeom Lee, Eunji Kim, and Sungroh Yoon, CVPR 2021. [paper]

Installation

• We kindly refer to the offical implementation of IRN.
• This repository is tested on Ubuntu 18.04, with Python 3.6, PyTorch 1.4, pydensecrf, scipy, chaniercv, imageio, and opencv-python.

Usage

Step 1. Prepare Dataset

• Download PASCAL VOC 2012 benchmark: Download.

Step 2. Prepare pre-trained classifier

• Pre-trained model used in this paper: Download.
• You can also train your own classifiers following IRN.

Step 3. Obtain the pseudo ground-truth masks for PASCAL VOC train_aug images and evaluate them

bash get_mask_quality.sh

Step 4. Train a semantic segmentation network

• To train DeepLab-v2, we refer to deeplab-pytorch. However, this repo contains only COCO pre-trained model. We provide ImageNet pre-trained model for a fair comparison with the other methods.

Acknowledgment

This code is heavily borrowed from IRN, thanks jiwoon-ahn!