Code and result about APNet(IEEE TETCI)
'APNet: Adversarial-Learning-Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection'
Python 3.7, Pytorch 1.5.0+, Cuda 10.2, TensorboardX 2.1, opencv-python
RGB-T SOD Datasets can be found in: https://github.com/lz118/RGBT-Salient-Object-Detection
Evaluate tools: we use the matlab verison provide by Dengping Fan.
NEW： We provide saliency maps of of all compared methods in paper. baidu 提取码：zust or Google drive.
Test saliency maps in all datasets[predict]: baidu 提取码：vy3r or Google drive.
The pretrained model can be downloaded at[APNet.pth]: baidu 提取码：vy3r or Google drive.
PS: we resize the testing data to the size of 224 * 224 for quicky evaluate[GT for matlab], baidu 提取码：vy3r or Google drive.
@ARTICLE
{9583676, author={Zhou, Wujie and Zhu, Yun and Lei, Jingsheng and Wan, Jian and Yu, Lu},
journal={IEEE Transactions on Emerging Topics in Computational Intelligence},
title={APNet: Adversarial Learning Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection},
year={2021},
volume={},
number={},
pages={1-12},
doi={10.1109/TETCI.2021.3118043}}
The implement of this project is based on the code of ‘Cascaded Partial Decoder for Fast and Accurate Salient Object Detection, CVPR2019’and 'BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network' proposed by Wu et al and Deng et al.
Please drop me an email for further problems or discussion: zzzyylink@gmail.com or wujiezhou@163.com