[CVPR2025 Workshop] Domain Generalization through Attenuation of Domain-Specific Information

Reiji Saito, Kazuhiro Hotta
Meijo University, 🍣JAPAN🍜

Paper: https://openaccess.thecvf.com/content/CVPR2025W/DG-EBF/html/Saito_Domain_Generalization_through_Attenuation_of_Domain-Specific_Information_CVPRW_2025_paper.html

ADSI when using DINOv2

Setting	mIoU	Config	Log & Checkpoint
GTAV $\rightarrow$ Cityscapes	67.75	config	log & checkpoint
GTAV $\rightarrow$ BDD100K	61.38	config	log & checkpoint
GTAV $\rightarrow$ Mapillary	67.59	config	log & checkpoint

Citation

If you find our code or data helpful, please cite our paper:

@InProceedings{Saito_2025_CVPR,
    author    = {Saito, Reiji and Hotta, Kazuhiro},
    title     = {Domain Generalization through Attenuation of Domain-Specific Information},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops},
    month     = {June},
    year      = {2025},
    pages     = {6459-6468}
}

Environment Setup

To set up your environment, execute the following commands (※Please do not install mmsegmentation):

conda create -n ADSI -y
conda activate ADSI
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia -y
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"
pip install "mmdet>=3.0.0"
pip install xformers=='0.0.20' # optional for DINOv2
pip install -r requirements.txt
pip install future tensorboard

Dataset Preparation

The Preparation is similar as DDB.

Cityscapes: Download leftImg8bit_trainvaltest.zip and gt_trainvaltest.zip from Cityscapes Dataset and extract them to data/cityscapes.

Mapillary: Download MAPILLARY v1.2 from Mapillary Research and extract it to data/mapillary.

GTA: Download all image and label packages from TU Darmstadt and extract them to data/gta.

Prepare datasets with these commands:

cd ADSI
mkdir data
# Convert data for validation if preparing for the first time
python tools/convert_datasets/gta.py data/gta # Source domain
python tools/convert_datasets/cityscapes.py data/cityscapes
# Convert Mapillary to Cityscapes format and resize for validation
python tools/convert_datasets/mapillary2cityscape.py data/mapillary data/mapillary/cityscapes_trainIdLabel --train_id
python tools/convert_datasets/mapillary_resize.py data/mapillary/validation/images data/mapillary/cityscapes_trainIdLabel/val/label data/mapillary/half/val_img data/mapillary/half/val_label

(Optional) ACDC: Download all image and label packages from ACDC and extract them to data/acdc.

(Optional) UrbanSyn: Download all image and label packages from UrbanSyn and extract them to data/urbansyn.

The final folder structure should look like this:

ADSI
├── ...
├── checkpoints
│   ├── dinov2_vitl14_pretrain.pth
│   ├── dinov2_rein_and_head.pth
├── data
│   ├── cityscapes
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── bdd100k
│   │   ├── images
│   │   |   ├── 10k
│   │   │   |    ├── train
│   │   │   |    ├── val
│   │   ├── labels
│   │   |   ├── sem_seg
│   │   |   |    ├── masks
│   │   │   |    |    ├── train
│   │   │   |    |    ├── val
│   ├── mapillary
│   │   ├── training
│   │   ├── cityscapes_trainIdLabel
│   │   ├── half
│   │   │   ├── val_img
│   │   │   ├── val_label
│   ├── gta
│   │   ├── images
│   │   ├── labels
├── ...

Pretraining Weights

• Download: Download pre-trained weights from facebookresearch for testing. Place them in the project directory without changing the file name.
• Convert: Convert pre-trained weights for training or evaluation.

  python tools/convert_models/convert_dinov2.py checkpoints/dinov2_vitl14_pretrain.pth checkpoints/dinov2_converted.pth

  (optional for 1024x1024 resolution)

  python tools/convert_models/convert_dinov2.py checkpoints/dinov2_vitl14_pretrain.pth checkpoints/dinov2_converted_1024x1024.pth --height 1024 --width 1024

Evaluation

Run the evaluation:

python tools/test.py configs/dinov2/rein_dinov2_mask2former_512x512_bs1x4_ADSI.py checkpoints/dinov2_rein_and_head.pth --backbone dinov2_converted.pth

For most of provided release checkpoints, you can run this command to evluate

python tools/test.py /path/to/cfg /path/to/checkpoint --backbone /path/to/dinov2_converted.pth #(or dinov2_converted_1024x1024.pth)

Training

Start training in single GPU:

python tools/train.py configs/dinov2/rein_dinov2_mask2former_512x512_bs1x4_ADSI.py

Start training in multiple GPU:

PORT=12345 CUDA_VISIBLE_DEVICES=1,2,3,4 bash tools/dist_train.sh configs/dinov2/rein_dinov2_mask2former_512x512_bs1x4_ADSI.py NUM_GPUS

Acknowledgements

We sincerely appreciate Rein, mmsegmentation, and mmdetection for their wonderful implementations.