GETAM

implementation for 'GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation'

https://arxiv.org/abs/2112.02841

Step1: environment

• clone this repo

git clone https://github.com/weixuansun/GETAM.git

• optionally create a new environment python>=3.6
• install requirements.txt

pip install -r requirements.txt

optional: build python extension module for DenseEnergyLoss:

cd wrapper/bilateralfilter
python setup.py install

More details please see here

Step2: dataset preparation

pascal voc

• Images
• saliency map

MS-COCO 2014

• Images
• saliency map

Step3: train and inference

end-to-end training on pascal:

Training requires one GPU, you can change GPU setting accordingly.

CUDA_VISIBLE_DEVICES=7 python train_from_init.py --session_name getam_001 -n 1 -g 1 -nr 0 --max_epoches 20  --lr 0.04 --cls_step 0.5 --seg_lr_scale 0.1 --sal_loss True --backbone vitb_hybrid --address 1234 --voc12_root {path to pascal voc dataset} --saliencypath {path to saliency maps}

Inference on pascal

python test.py --weights {path to weight} --val True --backbone {backbone}

end-to-end training on COCO

coming soon

Checkpoints

Dataset	Backbone	mIoU(val)	mIoU(test)	Checkpoint
PASCAL VOC 2012	Vit_hybrid	71.7	72.3	Download
PASCAL VOC 2012	Vit	68.1	68.8	Download
PASCAL VOC 2012	deit	66.0	68.9	Download
PASCAL VOC 2012	deit_distilled	70.7	71.1	Download
COCO	vit_hybrid	36.4		Download

Acknowledgement

• Thanks for the saliency object detection code provided by UCNet
• Thanks for codebase provided by DPT
• Thanks for codebase provided by RRM

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if you use this paper, please kindly cite:

@article{sun2021getam,
  title={GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation},
  author={Sun, Weixuan and Zhang, Jing and Liu, Zheyuan and Zhong, Yiran and Barnes, Nick},
  journal={arXiv preprint arXiv:2112.02841},
  year={2021}
}