# AdvCAM

**Repository Path**: AI52CV/AdvCAM

## Basic Information

- **Project Name**: AdvCAM
- **Description**: Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation
论文地址：https://arxiv.org/abs/2103.08896
代码原地址：https://github.com/jbeomlee93/AdvCAM
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-04-02
- **Last Updated**: 2021-04-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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


Input Image   |  Initial CAM   | Successive Maps with adversarial climbing
:-------------------------:|:-------------------------:|:-------------------------:
![a](demo/2008_004430.jpg)  |  ![b](demo/2008_004430_noreg_c_idx_0_iter_0.jpg) | ![c](demo/2008_004430_gif.gif)

The implementation of Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation, Jungbeom Lee, Eunji Kim, and Sungroh Yoon, CVPR 2021. [[paper](https://arxiv.org/abs/2103.08896)]

# Installation

- We kindly refer to the offical implementation of [IRN](https://github.com/jiwoon-ahn/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](https://drive.google.com/file/d/1e-yprFZzOYDAehjyMVyC5en5mNq6Mjh4/view?usp=sharing).


#### Step 2. Prepare pre-trained classifier

- Pre-trained model used in this paper: [Download](https://drive.google.com/file/d/1G0UkgjA4bndGBw2YFCrBpv71M5bj86qf/view?usp=sharing).
- You can also train your own classifiers following [IRN](https://github.com/jiwoon-ahn/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](https://github.com/kazuto1011/deeplab-pytorch). However, this repo contains only COCO pre-trained model. We provide [ImageNet pre-trained model](https://drive.google.com/file/d/14soMKDnIZ_crXQTlol9sNHVPozcQQpMn/view?usp=sharing) for a fair comparison with the other methods.


## Acknowledgment
This code is heavily borrowed from [IRN](https://github.com/jiwoon-ahn/irn), thanks [jiwoon-ahn](https://github.com/jiwoon-ahn)!