PICR-Net_ACMMM2023

Mindspore implementation version of PICR-Net

Runmin Cong, Hongyu Liu, Chen Zhang*, Wei Zhang, Feng Zheng, Ran Song, and Sam Kwong, Point-aware Interaction and CNN-induced Refinement Network for RGB-D salient object detection, ACM International Conference on Multimedia (ACM MM), 2023.

Network

Our overall framework：

Relation Modeling

Requirement

Pleasure configure the environment according to the given version:

• python 3.8.5
• mindspore 2.1.0
• mindcv 0.2.2
• opencv-python 4.6.0.66
• numpy 1.19.2
• tensorboardx 2.5.1

We also provide ".yaml" files for conda environment configuration, you can download it from [Link], code: mvpl, then use conda env create -f requirement.yaml to create a required environment.

Data Preprocessing

Please follow the tips to download the processed datasets and pre-trained model:

Download RGB-D SOD dataset from [Link], code: mvpl.

├── RGBD_dataset
    ├── train
        ├── RGB
        ├── depth
        ├── GT
    ├── val
        ├── RGB
        ├── depth
        ├── GT
    ├── test
        ├── NJU2K
            ├── RGB
            ├── depth
            ├── GT
        ├── NLPR
            ├── RGB
            ├── depth
            ├── GT
        ...

Training and Testing

Training command :

python train.py

Testing command :

The trained model for PICR-Net can be download here: [Link], code: mvpl.

python test.py

Evaluation

We implement three metrics: MAE (Mean Absolute Error), F-Measure, S-Measure. We use Toolkit [Link] to obtain the test metrics.

Results

1. Qualitative results: we provide the saliency maps, you can download them from [Link], code: mvpl.
2. Quantitative results:

Bibtex

   Coming soon...

Contact Us

If you have any questions, please contact Runmin Cong at [email protected] or Hongyu Liu at [email protected].