The codebase contains the official code of our paper Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation, CVPR 2024.

In our research, we employ the plug-and-play Domain Adapter to investigate the Cross-Domain Few-Shot Segmentation (CDFSS) problem. By integrating the adapter into the initial stages of the baseline model, we aim to align images from various domains to a standardized source domain, thereby mitigating domain discrepancies across different domains.

You can follow PATNet to prepare the source domain and target domain datasets.

• PASCAL VOC2012:

  Download PASCAL VOC2012 devkit (train/val data):

  wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

  Download PASCAL VOC2012 SDS extended mask annotations from [Google Drive].

• Deepglobe:

  Home: http://deepglobe.org/

  Direct: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset

  Preprocessed Data: https://drive.google.com/file/d/10qsi1NRyFKFyoIq1gAKDab6xkbE0Vc74/view?usp=sharing

• ISIC2018:

  Home: http://challenge2018.isic-archive.com

  Direct (must login): https://challenge.isic-archive.com/data#2018

  Class Information: data/isic/class_id.csv

  Preprocess: After downloading the ISIC dataset, please utilize the "./data_util/ISIC_Split.py" script to preprocess the raw ISIC data.

• Chest X-ray:

  Home: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256233/

  Direct: https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels

• FSS-1000:

  Home: https://github.com/HKUSTCV/FSS-1000

  Direct: https://drive.google.com/file/d/16TgqOeI_0P41Eh3jWQlxlRXG9KIqtMgI/view

• Pretrained model: ResNet-50

• Base model trained in original SSP: Ori_SSP_trained_on_VOC.pth(Google drive||Baidu Netdisk)

Please place the "Ori_SSP_trained_on_VOC.pth", trained in SSP, under the directory "./outdir/models/", and put the "resnet50.pth" under "./outdir/models/pretrained/".

    DR_Adapter                 # current (project) directory
       ├── codes               # various codes
       ├── ./outdir            # (dir)pretrained model and output model 
       │       ├── Ori_SSP_trained_on_VOC.pth
       │       ├── pretrained/
       │         └── resnet50.pth
       └── ./data
           ├── VOCdevkit/                # PASCAL VOC2012 devkit
           │    └── VOC2012/            
           │         ├── Annotations/
           │         ├── ImageSets/
           │         ├── ...
           │         └── SegmentationClassAug/
           ├── chest/           
           │    └── LungSegmentation      # lung benchmark
           │
           ├── fss
           │    └── FSS-1000/            # fss benchmark
           │        ├── abacus/   
           │        ├── ...
           │        └── zucchini/
           ├── ISIC
           │    └── ISIC/                # ISIC benchmark
           └── deepglobe
                └── Deepglobe/            # deepglobe benchmark

python train.py --train_datapath ./data/VOCdevkit
                --shot 1
                --benchmark pascal
                --bsz 8
                --logpath "your_experiment_name"

python test.py --test_datapath {./data/fss, ./data/chest, ./data/ISIC, ./data/deepglobe}
               --benchmark {fss,lung,isic,deepglobe}
               --nshot {1, 5} 
               --checkpoint_path "./outdir/models/pascal/best_model.pth"

This codebase is built based on SSP's baseline code and we borrow PATNet's evaluation protocol for the different dataset. We thank SSP and PATNet and other FSS works for their great contributions.

@article{su2024domain,
  title={Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation},
  author={Su, Jiapeng and Fan, Qi and Lu, Guangming and Chen, Fanglin and Pei, Wenjie},
  journal={arXiv preprint arXiv:2404.10322},
  year={2024}
}

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