Awesome
Saliency-Aware Texture Smoothing
by Lei Zhu*, Xiaowei Hu*, Chi-Wing Fu, Jing Qin and Pheng-Ann Heng (* joint first authors)
Guided Non-local Block for Saliency Detection
This implementation is written by Xiaowei Hu at the Chinese University of Hong Kong.
Citation
@article{zhu2020saliency,
author = {Zhu, Lei and Hu, Xiaowei and Fu, Chi-Wing and Qin, Jing and Heng, Pheng-Ann},
title = {Saliency-Aware Texture Smoothing},
journal={IEEE Transactions on Visualization and Computer Graphics},
volume={26},
number={7},
pages={2471-2484},
year={2020}
}
Dataset
Saliency Dataset for Texture Smoothing (SDTS) can be downloaded from Google Drive.
Installation
-
Please download and compile our CF-Caffe.
-
Clone the GNLB repository, and we'll call the directory that you cloned as
GNLB-master
.git clone https://github.com/xw-hu/GNLB.git
-
Replace
CF-Caffe/examples/
byGNLB-master/examples/
. ReplaceCF-Caffe/data/
byGNLB-master/data/
.
Test
-
Enter the
examples/GNLB/
and runtest_saliency.m
in Matlab. -
Apply CRF to do the post-processing for each image.
The code for CRF can be found in https://github.com/Andrew-Qibin/dss_crf
*Note that please provide a link to the original code as a footnote or a citation if you plan to use it.
Train
-
Download the pre-trained ResNet-101 caffemodel on ImageNet.
Put this model inCF-Caffe/models/
. -
Enter the
examples/GNLB/GNLB/
Modify the image path intrain_val.prototxt
.
Modify the weight path intrain.sh
for different training sets (MSRA10K or SDTS) following our paper. -
Run
sh train.sh