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Domain-Separation-Graph-Neural-Networks-for-Saliency-Object-Ranking

Official implementation of the CVPR 2024 paper Domain Separation Graph Neural Networks for Saliency Object Ranking. <img src="https://github.com/Wu-ZJ/DSGNN/blob/main/resources/main.png"/>

Installation

Our code is primarily based on MMDetection. Please refer to the MMDetection Installation for installation instructions.

Dataset

Download the ASSR Dataset and IRSR Dataset.

Training

ASSR Dataset

For resnet-50 backbone model:

bash ./tools/dist_train.sh configs/mask2former_sor/mask2former_sor_r50_assr.py num_gpus --load-from pertrained_model_path

For swin-L backbone model:

bash ./tools/dist_train.sh configs/mask2former_sor/mask2former_sor_swin-l-int21k_assr.py num_gpus --load-from pertrained_model_path

IRSR Dataset

For resnet-50 backbone model:

bash ./tools/dist_train.sh configs/mask2former_sor/mask2former_sor_r50_irsr.py num_gpus --load-from pertrained_model_path

For swin-L backbone model:

bash ./tools/dist_train.sh configs/mask2former_sor/mask2former_sor_swin-l-int21k_irsr.py num_gpus --load-from pertrained_model_path

</details>

Testing

ASSR Dataset

For resnet-50 backbone model:

bash ./tools/dist_test.sh configs/mask2former_sor/mask2former_sor_r50_assr.py model_path 1 --eval mae

For swin-L backbone model:

bash ./tools/dist_test.sh configs/mask2former_sor/mask2former_sor_swin-l-int21k_assr.py model_path 1 --eval mae

IRSR Dataset

For resnet-50 backbone model:

bash ./tools/dist_test.sh configs/mask2former_sor/mask2former_sor_r50_irsr.py model_path 1 --eval mae

For swin-L backbone model:

bash ./tools/dist_test.sh configs/mask2former_sor/mask2former_sor_swin-l-int21k_irsr.py model_path 1 --eval mae

Pretrained Models

Model	Dataset	Download
Pertrained-Res50	COCO	mask2former_r50_lsj_8x2_50e_coco
Pertrained-SwinL	COCO	mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic

Results

Model	Dataset	SA-SOR	Download
DSGNN-Res50	ASSR	0.716	model (3qm5) \| visualization results (d8m1)
DSGNN-SwinL	ASSR	0.761	model (1pjw) \| visualization results (9esz)
DSGNN-Res50	IRSR	0.569	model (mfdh)
DSGNN-SwinL	IRSR	0.607	model (sq1r)

Citation

@InProceedings{Wu_2024_CVPR,
    author    = {Wu, Zijian and Lu, Jun and Han, Jing and Bai, Lianfa and Zhang, Yi and Zhao, Zhuang and Song, Siyang},
    title     = {Domain Separation Graph Neural Networks for Saliency Object Ranking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {3964-3974}
}