Awesome

TransCAM

This is the official repo of the paper: TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation

Accepted to Journal of Visual Communication and Image Representation 2023

Prerequisite

1. install dependencies

pip install -r requirements.txt

2. Download pretrained model

Download Conformer-S pretrained weights from https://github.com/pengzhiliang/Conformer

Download ResNet-38 pretained weights from https://github.com/YudeWang/SEAM

Usage

TransCAM step

1. train the conformer classifier

python train_TransCAM.py --weights {pretrained_conformer_weights}

You can also download our trained weights here

2. TransCAM inference

python infer_TransCAM.py --weights {trained_weights}

We provide a notebook for visualizing TransCAM result: visualize.ipynb

3. TransCAM evaluation

python evaluation.py --comment transcam --predict_dir data/transcam/out_cam

PSA step

1. train AffinityNet

python train_aff.py

2. RW propagation

python infer_aff.py

3. RW evaluation

python evaluation.py --comment transcam_rw --predict_dir data/transcam/out_rw --type png

DeepLab step

1. train DeepLab

python train_deeplab.py

2. test DeepLab

python test_deeplab.py

Citation

@article{li2023transcam,
  title={Transcam: Transformer attention-based cam refinement for weakly supervised semantic segmentation},
  author={Li, Ruiwen and Mai, Zheda and Zhang, Zhibo and Jang, Jongseong and Sanner, Scott},
  journal={Journal of Visual Communication and Image Representation},
  pages={103800},
  year={2023},
  publisher={Elsevier}
}

Acknowledge

This repo is developed based on SEAM [1] and Conformer [2]

[1] Wang, Yude, et al. "Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

[2] Peng, Zhiliang, et al. "Conformer: Local features coupling global representations for visual recognition." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.