Skip to content

Latest commit

 

History

History
62 lines (47 loc) · 3.67 KB

README.md

File metadata and controls

62 lines (47 loc) · 3.67 KB
Raw

Learning to Detect Salient Objects with Image-level Supervision

Introduction

WSS is a weakly-supervised saliency detection method with fully convolutional neural networks. This package contains the source code to reproduce the experimental results of WSS reported in our CVPR 2017 paper. The source code is mainly written in MATLAB with the Caffe MATLAB wrapper.

Usage

  • Supported OS: the source code was tested on 64-bit Ubuntu 14.04 Linux OS, and it should also be executable in other linux distributions.

  • Dependencies:

  • Our home-brewed Caffe framework and all its dependencies.

  • Cuda enabled GPUs

  • Installation:

  1. Clone from github via: git clone --recursive https://github.com/scott89/WSS.git
  2. Install caffe-cvpr17: caffe-cvpr17 is our home-brewed version of the original caffe. Change directory into ./caffe-cvpr17 and compile the source code and the matlab interface following the installation instruction of caffe.
  3. Download the trained caffe model from https://pan.baidu.com/s/1gfxSbSJ, and put both the caffemodel and prototxt files under the ./model directory.
  4. Run the demo code test_sal.m. The predicted saliency maps are saved in the sal_res directory.

Citing Our Work

If you find WSS useful in your research, please consider to cite our paper:

    @inproceedings{wang2017,
       author = {Wang, Lijun and Lu, Huchuan and Wang, Yifan and Feng, Mengyang and Wang, Dong and Yin, Baocai and Ruan, Xiang},
       title = {Learning to Detect Salient Objects With Image-Level Supervision},
       booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
       year = {2017}
    }

Liscense

    Copyright (c) 2015, Lijun Wang
    All rights reserved. 
    Redistribution and use in source and binary forms, with or without modification, are 
    permitted provided that the following conditions are met:
		* Redistributions of source code must retain the above copyright 
  		  notice, this list of conditions and the following disclaimer.
		* Redistributions in binary form must reproduce the above copyright 
  		  notice, this list of conditions and the following disclaimer in 
  		  the documentation and/or other materials provided with the distribution
    
    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 
    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 
    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 
    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 	
    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 
    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 
    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 
    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 
    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 
    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 
    POSSIBILITY OF SUCH DAMAGE.