Code and result about CCAFNet(IEEE TMM)
'CCAFNet: Crossflow and Cross-scale Adaptive Fusion Network for Detecting Salient Objects in RGB-D Images' IEEE TMM

Requirements

Python 3.7, Pytorch 1.5.0+, Cuda 10.2, TensorboardX 2.1, opencv-python

Dataset and Evaluate tools

RGB-D SOD Datasets can be found in: http://dpfan.net/d3netbenchmark/ or https://github.com/jiwei0921/RGBD-SOD-datasets

we use the matlab verison provide by Dengping Fan, and we provide our test datesets 百度网盘提取码：zust

Result

Test maps: 百度网盘提取码：zust
Pretrained model download:百度网盘提取码：zust
PS: we resize the testing data to the size of 224 * 224 for quicky evaluate, 百度网盘提取码：zust

Citation

@ARTICLE{9424966,
author={Zhou, Wujie and Zhu, Yun and Lei, Jingsheng and Wan, Jian and Yu, Lu},
journal={IEEE Transactions on Multimedia},
title={CCAFNet: Crossflow and Cross-scale Adaptive Fusion Network for Detecting Salient Objects in RGB-D Images},
year={2021},
doi={10.1109/TMM.2021.3077767}}

Acknowledgement

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.

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