Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

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Deep LearningDRS
Overview

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation (AAAI 2021)

Official pytorch implementation of our paper: Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation [Paper], Beomyoung Kim, Sangeun Han, and Junmo Kim, AAAI 2021

PWC PWC

We propose the discriminative region suppression (DRS) module that is a simple yet effective method to expand object activation regions. DRS suppresses the attention on discriminative regions and spreads it to adjacent non-discriminative regions, generating dense localization maps.

DRS module

Setup

  1. Dataset Preparing

    # dataset structure
    VOC2012/
        --- Annotations/
        --- ImageSets/
        --- JPEGImages/
        --- SegmentationClassAug/
        --- saliency_map/
        --- refined_pseudo_segmentation_labels/
    
  2. Requirements pip install -r requirements.txt

Training & Pseudo Segmentation Labels Generation

  • step1 : training the classifier with DRS modules
  • step2 : training the refinement network for the localization maps refinement
  • step3 : pseudo segmentation labels generation
# all-in-one
bash run.sh 
Model pretrained
VGG-16 with the learnable DRS DRS_learnable/best.pth
Refinement network Refine_DRS_learnable/best.pth
Pseudo Segmentation Labels refined_pseudo_segmentation_labels/

Training the DeepLab-V2 using pseudo labels

We adopt the DeepLab-V2 pytorch implementation from https://github.com/kazuto1011/deeplab-pytorch.

cd DeepLab-V2-PyTorch/

# motify the dataset path (DATASET.ROOT)
vi configs/voc12.yaml

# 1. training the DeepLab-V2 using pseudo labels
bash train.sh

# 2. evaluation the DeepLab-V2
bash eval.sh
Model mIoU mIoU + CRF pretrained
DeepLab-V2 with ResNet-101 69.4% 70.4% [link]
  • Note that the pretrained weight path ./DeepLab-V2-Pytorch/data/models/Deeplabv2_pseudo_segmentation_labels/deeplabv2_resnet101_msc/train_cls/checkpoint_final.pth
  • According to the DeepLab-V2 pytorch implementation we used, we requires an initial weights [download].

Citation

We hope that you find this work useful. If you would like to acknowledge us, please, use the following citation:

@inproceedings{kim2021discriminative,
    title={Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation},
    author={Kim, Beomyoung and Han, Sangeun and Kim, Junmo},
    year={2021},
    booktitle={AAAI Conference on Artificial Intelligence},
}
Owner
Beom
Computer Vision & Deep Learning
Beom
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