DCDNet

KO | EN

gitlite — search

#hacktoberfest #react #android #machine-learning #rust #deep-learning #linux #docker #nodejs #kubernetes #awesome-list #pytorch #mcp #openai

(★ 28)

【AAAI 2026 Oral】The official implementation of "Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation".

DCDNet Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# [AAAI 2026 Oral] Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation

The official implementation of "Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation".

More detailed information is in the [PAPER](https://ojs.aaai.org/index.php/AAAI/article/view/37338).

Authors: Runmin Cong, Anpeng Wang, Bin Wan, Cong Zhang, Xiaofei Zhou, Wei Zhang

## Datasets
The following datasets are used for evaluation in CD-FSS:

### Source domain:
* **PASCAL VOC2012**:

Download PASCAL VOC2012 devkit (train/val data):
    ```bash
    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](https://drive.google.com/file/d/10zxG2VExoEZUeyQl_uXga2OWHjGeZaf2/view?usp=sharing)].

### Target domains:
* **Deepglobe**:

Home: http://deepglobe.org/

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

Data Preprocessing Code: Please refer preprocess_deepglobe.py or [PATNet repo](https://github.com/slei109/PATNet).

* **ISIC2018**:

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

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

Class Information: data/isic/class_id.csv

Data Preprocessing Code: Please refer preprocess_isic.py or [PATNet repo](https://github.com/slei109/PATNet).

* **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

For convenience, you can also download the target domain datasets directly from our organized [Baidu Netdisk](https://pan.baidu.com/s/1_GUff9OX03S-SyRBjXHFJQ?pwd=vc29).

## Pre-trained ResNet Models

Download pre-trained ResNet models: [GoogleDrive](https://drive.google.com/file/d/1HlyNsgYr9RYbbpPrE7FB6bc_2wCPBEx_/view?usp=sharing) or [Baidu Netdisk](https://pan.baidu.com/s/1yOUPfa_DFNhT6EoxG5Mcqg?pwd=5yx8).

Download SSP pre-trained models: [GoogleDrive](https://drive.google.com/file/d/1NBpgKctAx9h27XdnAOLXlyjdSVrx5EG6/view?usp=sharing) or [Baidu Netdisk](https://pan.baidu.com/s/1EkETN-GNUR0VQZHN81C1bw?pwd=jr9n).

### File Organization
```
    DCDNet/                                             # project file
    ├── dataset/                                        # dataset
    |   ├── VOC2012/                                    # source dataset: pascal voc 2012
    |   |   ├── JPEGImages/
    |   |   └── SegmentationClassAug/
    |   ├── Deepglobe                                   # target dataset: deepglobe
    |   |   ├── 1/                                      # category
    |   |   |   └── test/
    |   |   |       ├── origin/                         # image
    |   |   |       └── groundtruth/                    # mask
    |   |   ├── 2/                                      # category
    |   |   └── ...                                     
    |   ├── ISIC                                        # target dataset: isic
    |   |   ├── ISIC2018_Task1-2_Training_Input/        # image
    |   |   |   ├── 1/                                  # category
    |   |   |   └── ...
    |   |   └── ISIC2018_Task1_Training_GroundTruth/    # mask
    |   |       └── ...
    |   ├── LungSegmentation/                           # target dataset: chest x-ray
    |   |   ├── CXR_png/                                # image
    |   |   └── masks/                                  # mask
    |   └── FSS-1000                                    # target dataset: fss-1000
    |       ├── ab_wheel/                               # category
    |       └── ...
    |
    ├── pretrained/                                     # pretrained resnet models
    |   ├── resnet50.pth
    |   └── Ori_SSP_trained_on_VOC.pth
    |
    └── trained_models/                                 # official trained models
        ├── deepglobe/                                  # target dataset
        └── ...
```

## Environment

Conda environment settings:

```bash
conda create -n dcdnet python=3.10
conda activate dcdnet

pip install uv

# Install PyTorch 2.2.0 with CUDA 12.1
uv pip install torch==2.2.0 torchvision==0.17.0 torchaudio --index-url https://download.pytorch.org/whl/cu121

uv pip install -r requirements.txt
```

## Run the code

Here is an example on ISIC dataset:

First, you need to train a model on the source dataset:
```
python train.py --data-root ./dataset --dataset isic --cuda 0
```

Then, you need to fine-tuning the trained model on the target dataset:
```
python finetuning.py  --data-root ./dataset --dataset isic --cuda 0
```

You can use our trained models for evaluation directly:
```
python test.py --data-root ./dataset --dataset isic --cuda 0
```

Please note that the performances may flutuate within a small range because of differnet batch-sizes, seeds, devices, and environments.

## Citation

If you use this codebase for your research, please consider citing:

```
@inproceedings{cong2026divide,
  title={Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation},
  author={Cong, Runmin and Wang, Anpeng and Wan, Bin and Zhang, Cong and Zhou, Xiaofei and Zhang, Wei},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={5},
  pages={3416--3424},
  year={2026}
}
```

## Acknowledgement
Our codebase is built based on [IFA](https://github.com/niejiahao1998/IFA) and [SSP](https://github.com/fanq15/SSP)'s official code.

We also thank [PATNet](https://github.com/slei109/PATNet) and other FSS and CD-FSS works for their great contributions.

## Reference

[1] Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Bowen Du, and Chang-Tien Lu. Cross-domain few-shot semantic segmentation. ECCV, 2022.

[2] Jiahao Nie,  Yun Xing, Gongjie Zhang, Pei Yan, Aoran Xiao, Yap-Peng Tan, Alex C Kot, Shijian Lu. Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining. CVPR, 2024.

DCDNet

DCDNet Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

DCDNet

DCDNet Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README