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Learning Integral Objects with Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation

ICD 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Learning Integral Objects with Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation
Learning Integral Objects with Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation, Junsong Fan, Zhaoxiang Zhang, Chunfeng Song, Tieniu Tan, CVPR2020 [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Learning_Integral_Objects_With_Intra-Class_Discriminator_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2020_paper.pdf).

## Introduction

The previous class activation map (CAM) based approach learns inter-class boundaries, which focus on the difference between different foreground classes. It may not be optimal for the weakly supervised segmentation problem because the target object and the background share the same classes in the same image. To alleviate this problem, we propose an ICD approach to learning per-category intra-class boundaries between the foreground objects and the background, which is more appropriate for weakly supervised segmentation problems.

![introduction](https://raw.githubusercontent.com/js-fan/ICD/master/resources/introduction.jpg)

The proposed approach is end-to-end and can be trained together with the CAM branch in a single round. The pseudo-masks derived by the proposed ICD is more complete than the CAM based results.

![framework](https://raw.githubusercontent.com/js-fan/ICD/master/resources/framework.jpg)

![visualization](https://raw.githubusercontent.com/js-fan/ICD/master/resources/visualization.jpg)

## Prerequisite

- Python 3.7, MXNet 1.3.1, Numpy, OpenCV, [pydensecrf](https://github.com/lucasb-eyer/pydensecrf)
- NVIDIA GPU

## Usage

**Prepare the data and pretrained parameters:**

- Download the VOC 2012 dataset, which should contain `VOC2012/JPEGImages` and `VOC2012/Annotations`.
- Download the ImageNet pretrained parameters for the [VGG16 backbone](https://drive.google.com/file/d/1SVQgdnuhL7Wo3XIDfwhjVMTk_N1qiVHw/view?usp=sharing) and put it in the folder `ICD/data/pretrained`
- Download the pre-computed superpixels [here](https://drive.google.com/file/d/1vciTj1z32JF6CwNKBc87ypGDLgb8oIQ3/view?usp=sharing), untar it, and put it into the folder `ICD/data/superpixels`. You can also generate them by yourself, to do this, please follow the following commands:

```
cd external/superpixel
make -j8   ## relies on OpenCV, edit Makefile if necessary.
./makeSuperpixel ../../data/VOC2012/train_aug.txt Your/VOC2012/JPEGImages ./data/VOC 75
cd ../..
python generate_superpixel.py
```

**Train ICD and generate pseudo-masks:**

```
python run_icd.py --gpus 0,1 --image-root Your/VOC2012/JPEGImages --annotation-root Your/VOC2012/Annotations --superpixel-root ./data/superpixels/voc_superpixels
```

The above script contains codes for training and generating the seeds. By default, the seeds will be generated at `./snapshot/icd/results/seeds/crf`. After obtaining the seeds, you can take them as pseudo labels and train any segmentation models on the training set, for example, the DeepLab-v2 used in the paper. Codes to train the Deeplab-v2 models will also be released soon.

**Generate seeds with saliency maps:**

To generate seeds with the saliency, you need pre-computed saliency maps firstly. We adopt the same saliency maps used by the [OAA project](https://github.com/PengtaoJiang/OAA), and the saliency maps can be downloaded [here](https://drive.google.com/file/d/1MVbisWvcikdg_hRuK5gbhtGDV-g3TJXd/view?usp=sharing). To generate seeds with saliency maps, use the `--use-sal` flag, e.g.,

```
python run_icd.py --image-root Your/VOC2012/JPEGImages --annotation-root Your/VOC2012/Annotations --use-sal --only-gen-seeds
```

Seeds will be placed in `./snapshot/icd/results/seeds/sal` by default.

## Citation

```
@InProceedings{Fan_2020_CVPR,
author = {Fan, Junsong and Zhang, Zhaoxiang and Song, Chunfeng and Tan, Tieniu},
title = {Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
```

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