Awesome
HAINet
This project provides the code and results for 'Hierarchical Alternate Interaction Network for RGB-D Salient Object Detection', IEEE TIP 2021. Paper link Homepage
Network Architecture
<div align=center> <img src="https://github.com/MathLee/HAINet/blob/main/Images/NetworkOverview.png"> </div>Requirements
python2.7
pytorch 0.4.0
Our code is implemented based on the environment settings of CPD.
Usage
Modify the paths of VGG backbone (code: ego5) and datasets, then run train_HAI.py or test_HAI.py
Pre-trained model
Trained with NJU2K and NLPR (code: 4ntl)
Trained with NJU2K, NLPR and DUTLF-Depth (code: ae49)
RGB-D SOD Results Trained with NJU2K and NLPR
We provide results (code: a2as) of our HAINet on 5 datasets (STEREO1000, NJU2K, DES, NLPR and SIP) and additional 2 datasets (SSD and LFSD).
RGB-D SOD Results Trained with NJU2K, NLPR and DUTLF-Depth
We provide results (code: n35b) of our HAINet on 7 datasets (STEREO1000, NJU2K, DES, NLPR, SIP, DUTLF-Depth and ReDWeb-S).
RGB-T SOD Results
We apply our HAINet to RGB-T SOD, and provide results (code: s82s) of our HAINet on VT821 dataset trained with VT1000 dataset.
<div align=center> <img src="https://github.com/MathLee/HAINet/blob/main/Images/Table3.png"> </div>Evaluation Tool
You can use the evaluation tool to evaluate the above saliency maps.
Related works on RGB-D SOD
(ECCV_2020_CMWNet) Cross-Modal Weighting Network for RGB-D Salient Object Detection.
(TIP_2020_ICNet) ICNet: Information Conversion Network for RGB-D Based Salient Object Detection.
(Survey) RGB-D Salient Object Detection: A Survey.
Citation
@ARTICLE{Li_2021_HAINet,
author = {Gongyang Li and Zhi Liu and Minyu Chen and Zhen Bai and Weisi Lin and Haibin Ling},
title = {Hierarchical Alternate Interaction Network for RGB-D Salient Object Detection},
journal = {IEEE Transactions on Image Processing},
year = {2021},
volume = {30},
pages = {3528-3542},}
If you encounter any problems with the code, want to report bugs, etc.
Please contact me at lllmiemie@163.com or ligongyang@shu.edu.cn.