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https://github.com/YudeWang/SEAM

Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation, CVPR 2020 (Oral)
https://github.com/YudeWang/SEAM

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Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation, CVPR 2020 (Oral)

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# SEAM

The implementation of [**Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentaion**](https://arxiv.org/abs/2004.04581).



You can also download the repository from [https://gitee.com/hibercraft/SEAM](https://gitee.com/hibercraft/SEAM)



## Abstract

Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recentyears. Most of advanced solutions exploit class activation map (CAM). However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. In this paper, we propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation. However, this constraint is lost on the CAMs trained by image-level supervision. Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. Moreover, we propose a pixel correlation module (PCM), which exploits context appearance information and refines the prediction of current pixel by its similar neighbors, leading to further improvement on CAMs consistency. Extensive experiments on PASCAL VOC 2012 dataset demonstrate our method outperforms state-of-the-art methods using the same level of supervision.



Thanks to the work of [jiwoon-ahn](https://github.com/jiwoon-ahn), the code of this repository borrow heavly from his [AffinityNet](https://github.com/jiwoon-ahn/psa) repository, and we follw the same pipeline to verify the effectiveness of our SEAM.



## Requirements

- Python 3.6

- pytorch 0.4.1, torchvision 0.2.1

- CUDA 9.0

- 4 x GPUs (12GB)



## Usage

### Installation

- Download the repository.

```

git clone https://github.com/YudeWang/SEAM.git

```

- Install python dependencies.

```

pip install -r requirements.txt

```

- **Download model weights from [google drive](https://drive.google.com/open?id=1jWsV5Yev-PwKgvvtUM3GnY0ogb50-qKa) or [baidu cloud](https://pan.baidu.com/s/1ymaMeF0ASjQ9oCGI9cmqHQ) (with code 6nmo)**, including ImageNet pretrained models and our training results.



- Download PASCAL VOC 2012 devkit (follow instructions in http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit). It is suggested to make a soft link toward downloaded dataset.

```

ln -s $your_dataset_path/VOCdevkit/VOC2012 VOC2012

```



- (Optional) The image-level labels have already been given in `voc12/cls_label.npy`. If you want to regenerate it (which is unnecessary), please download the annotation of VOC 2012 SegmentationClassAug training set (containing 10582 images), which can be download [here](https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip?dl=0) and place them all as `VOC2012/SegmentationClassAug/xxxxxx.png`. Then run the code

```

cd voc12

python make_cls_labels.py --voc12_root VOC2012

```

### SEAM step



1. SEAM training

```

python train_SEAM.py --voc12_root VOC2012 --weights $pretrained_model --session_name $your_session_name

```



2. SEAM inference. 

```

python infer_SEAM.py --weights $SEAM_weights --infer_list [voc12/val.txt | voc12/train.txt | voc12/train_aug.txt] --out_cam $your_cam_dir --out_crf $your_crf_dir

```



3. SEAM step evaluation. We provide python mIoU evaluation script `evaluation.py`, or you can use official development kit. Here we suggest to show the curve of mIoU with different background score.

```

python evaluation.py --list VOC2012/ImageSets/Segmentation/[val.txt | train.txt] --predict_dir $your_cam_dir --gt_dir VOC2012/SegmentationClass --comment $your_comments --type npy --curve True

```



### Random walk step

The random walk step keep the same with AffinityNet repository.

1. Train AffinityNet.

```

python train_aff.py --weights $pretrained_model --voc12_root VOC2012 --la_crf_dir $your_crf_dir_4.0 --ha_crf_dir $your_crf_dir_24.0 --session_name $your_session_name

```

2. Random walk propagation

```

python infer_aff.py --weights $aff_weights --infer_list [voc12/val.txt | voc12/train.txt] --cam_dir $your_cam_dir --voc12_root VOC2012 --out_rw $your_rw_dir

```

3. Random walk step evaluation

```

python evaluation.py --list VOC2012/ImageSets/Segmentation/[val.txt | train.txt] --predict_dir $your_rw_dir --gt_dir VOC2012/SegmentationClass --comment $your_comments --type png

```



### Pseudo labels retrain

Pseudo label retrain on DeepLabv1. Code is available [here](https://github.com/YudeWang/semantic-segmentation-codebase/tree/main/experiment/seamv1-pseudovoc).



## Citation

Please cite our paper if the code is helpful to your research.

```

@InProceedings{Wang_2020_CVPR_SEAM,

    author = {Yude Wang and Jie Zhang and Meina Kan and Shiguang Shan and Xilin Chen},

    title = {Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation},

    booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

    year = {2020}

}

```

## 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.