Awesome
Cross-modal Hierarchical Interaction Network for RGB-D Salient Object Detection
Figure.1 The overall architecture of the proposed HINet model.
The paper can be downloaded from here[code:NEPU], which is published in Patern Recognition 🎆.
1.Requirements
Python v3.6, Pytorch 0.4.0+, Cuda 10.0, TensorboardX 2.0, opencv-python
2.Data Preparation
Download the test data from here[code:NEPU], test_in_train data from here[code:NEPU]. Then put them under the following directory:
-Dataset\
-train\
-test\
-NLPR\
-STERE\
-SSD\
-LFSD\
-NJUD\
-test_in_train\
3.Training/Testing & Evaluating
- Training the HINet
Please download the released code and then:
run python Train.py
- Testing the HINet
Please complete the training or download the pre-trained weights from here[code:NEPU] or Google, and then:
run python Test.py
Then the test maps will be saved to './Salmaps/'
- Evaluate the result maps
You can evaluate the result maps using the tool from here[code:NEPU], thanks for Dengpin Fan.
4.Results
- Qualitative comparison
Figure.2 Qualitative comparison of our proposed method with some SOTA methods.
- Quantitative comparison
Table.1 Quantitative comparison with some SOTA models on five public RGB-D benchmark datasets.
5.Citation
Thank you for your interest in our work, please cite:
@article{BI2022109194, title = {Cross-modal Hierarchical Interaction Network for RGB-D Salient Object Detection}, journal = {Pattern Recognition}, pages = {109194}, year = {2022}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2022.109194}, url = {https://www.sciencedirect.com/science/article/pii/S0031320322006732}, }
6.Contact
If you have any questions, feel free to contact us via tianzhu.xiang19@gmail.com (T.-Z. Xiang) or wuranwan2020@sina.com (Ranwan Wu). For more related work, you can also visit tianzhu.xiang