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.

• Training:

  If you are interested in the training code, you can find the pre-released version at the following two repositories: https://github.com/wanglijun1989/caffe-cvpr17
  https://github.com/wanglijun1989/gt-estimation-cvpr17

  However, this version is currently not well documented. A more friendly and easier to use version will be relased in the near future.

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
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