Awesome

Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation

Network Architecture

The architecture of our proposed approach is as follows

Prerequisite

• Tested on Ubuntu 18.04, with Python 3.8, PyTorch 1.8.2, CUDA 11.3.

• You can create conda environment with the provided yaml file.

conda env create -f wsss_new.yaml

• Download The PASCAL VOC 2012 development kit: and put it under ./data/ folder.

Test KTSE

• Download our pretrained weight 039net_main.pth (PASCAL, seed: 67% mIoU) and put it under ./experiments/ktse1/ckpt/ folder.

python infer.py --name ktse1 --model ktse --load_epo 39 --dict  --infer_list voc12/train_aug.txt

python evaluation.py --name ktse1 --task cam --dict_dir dict

Train KTSE

• Download the initial weights pretrained on Imagenet ilsvrc-cls_rna-a1_cls1000_ep-0001.params and put it under ./pretrained/ folder.
• Please specify the name of your experiment (e.g., ktse1).

python train.py --name ktse1 --model ktse

Second stage training or testing for the segmentation network BECO

Testing the segmentation results with our pretrained model directly

Prerequisite for the segmentation task

• Install Python 3.8, PyTorch 1.11.0, and more in requirements.txt

• Download ImageNet pretrained model of DeeplabV2 from pytorch . Rename the downloaded pth as "resnet-101_v2.pth" and put it into the directory './data/model_zoo/'. (This step is just to avoid directory related error.)

• Download our generated pseudo label sem_seg and put it into the directory './data/'. (This step is just to avoid directory related error.)

• Download our pretrained checkpoint best_ckpt_KTSE_73.0.pth and put it into the directory './segmentation/'. Test the segmentation network (you need to install CRF python library (pydensecrf) if you want to test with the CRF post-processing)

cd segmentation
pip install -r requirements.txt 

python main.py --test --logging_tag seg_result --ckpt best_ckpt_KTSE_73.0.pth
python test.py --crf --logits_dir ./data/logging/seg_result/logits_msc --mode "val"

Refine the seed for pseudo label with IRN

• Put the downloaded pretrained weight 039net_main.pth into the ./irn/sess/ directory.
• Run run_sample.py (You can either mannually edit the file, or specify commandline arguments.) and gen_mask.py to obtain the pseudo-labels and confidence masks (put them into the directory './segmentation/data/' ). Our generated ones can also be downloaded from sem_seg and mask_irn .

cd irn 
python run_sample.py
python gen_mask.py

Training the segmentation network

Prepare the data directory

• Put the data and pretrained model in the corresponding directories like:

data/
    --- VOC2012/
        --- Annotations/
        --- ImageSet/
        --- JPEGImages/
        --- SegmentationClass/
        --- ...
    --- sem_seg/
        --- ****.png
        --- ****.png
    --- mask_irn/
        --- ****.png
        --- ****.png
    --- model_zoo/
        --- resnet-101_v2.pth
    --- logging/

• Train the segmentation network

cd segmentation
python main.py -dist --logging_tag seg_result --amp

Acknowledgements

This code is heavily borrowed from AEFT, IRN and BECO.

Citation

If you find this useful in your research, please consider citing:

@article{chen2024knowledge,
title={Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation},
author={Chen, Tao and Jiang, Xiruo and Pei, Gensheng and Sun, Zeren and Wang, Yucheng and Yao, Yazhou},
journal={European Conference on Computer Vision (ECCV)},
year={2024}
}