CMWNet
This project provides the codes and results for 'Cross-Modal Weighting Network for RGB-D Salient Object Detection', ECCV 2020. Paper link.
Our code is implemented based on the Caffe of FlowNet2. You should first install and compile the caffe according to the FlowNet2.
Overview and Module details
Overview:
Module details:
Saliency maps and Measure results on 8 Datasets
We provide saliency maps (code: 6f2j) and measure results (code: p0y7) of our CMWNet on 8 datasets (STEREO, NJU2K, LFSD, DES, NLPR, SSD, SIP and additional DUT-RGBD). You can use the evaluation tool to evaluate the result maps.
Testing
test_RGBD.prototxt/
is undermodels/
.- Download the trained model (code: z2o4) (
RGBD_iter_22500.caffemodel
), and put it undermodels/
. - The datasets are under
datasets/
, we provide some testing examples on DES dataset. - Run
test_matlab/test_CMWNet.m
. - Saliency maps are under
salmaps/DES/
.
Related works on RGB-D SOD
(TIP_2020_ICNet) ICNet: Information Conversion Network for RGB-D Based Salient Object Detection.
(Survey) RGB-D Salient Object Detection: A Survey.
Citation
@inproceedings{Li_2020_CMWNet,
author = {Li, Gongyang and Liu, Zhi and Ye, Linwei and Wang, Yang and Ling, Haibin},
title = {Cross-Modal Weighting Network for RGB-D Salient Object Detection},
journal = {European Conference on Computer Vision (ECCV)},
pages = {665-681},
month = {Aug.},
year = {2020},}
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.