Awesome
PICR-Net_ACMMM2023
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 3.6.13
- torch 1.8.2
- torchvision 0.9.2
- opencv-python 4.6.0.66
- numpy 1.19.5
- timm 0.6.7
- einops 0.3.2
- 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.
Download pretrained backbone weights from [Link], code: mvpl.
├── RGBD_dataset
├── train
├── RGB
├── depth
├── GT
├── val
├── RGB
├── depth
├── GT
├── test
├── NJU2K
├── RGB
├── depth
├── GT
├── NLPR
├── RGB
├── depth
├── GT
...
├── pretrain
├── swin_tiny_patch4_window7_224.pth
├── vgg16_bn-6c64b313.pth
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
- Qualitative results: we provide the saliency maps, you can download them from [Link], code: mvpl.
- Quantitative results:
Bibtex
@inproceedings{PICR-Net,
title={Point-aware Interaction and {CNN}-induced Refinement Network for {RGB-D} salient object detection},
author={Cong, Runmin and Liu, Hongyu and Zhang, Chen and Zhang, Wei and Zheng, Feng and Song, Ran and Kwong, Sam },
journal={ACM International Conference on Multimedia (ACM MM) },
year={2023},
}
Contact Us
If you have any questions, please contact Runmin Cong at rmcong@sdu.edu.cn or Hongyu Liu at liu.hongyu@bjtu.edu.cn.