Giter Club home page Giter Club logo

wscnntdsaliency's Introduction

Codes for Weakly Supervised Saliency Detection

Contact: Kuang-Jui Hsu

Last update: 2017/12/30

Platform: Ubuntu 14.04, MatConvnet 1.0-beta24 (We don's support any installation problem of MatConvnet.)


Paper: [BMVC17] Weakly Supervised Saliency Detection with A Category-Driven Map Generator

Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang

PDF: Link1, Link2

Please cite our paper if this code is useful for your research.


@inproceedings{HsuBMVC17,
  author = {K.-J. Hsu and Y.-Y. Lin and Y.-Y Chuang},
  booktitle = {British Machine Vision Conference (BMVC)},
  title = {Weakly Supervised Saliency Detection with A Category-Driven Map Generator},
  year = {2017}
}

Demo for training and test: "Run.m"

  • This code is only for demo and different from the origianl code because some files were overwrited when I worked for the journal extension.

  • The output size of the generator is W * H * 1 after a sigmoid normalization instead of W * H * 2 after a softmax normalization.

  • The parameters for weighting losses are not tuned.

  • The random seed and the number of total epoches may effect the performance.

  • The results are slightly better than ones reported in [BMVC'17] for Graz02 Dataset.

Bike Car Person Mean
80.4 63.1 66.5 70.0

Results used in BMVC'17:


Link: Some results in the journal extension (will be released until it is accepted).

  • Faster speed:

  • Better results:

wscnntdsaliency's People

Contributors

kuangjuihsu avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

Forkers

hyzcn kingfou

wscnntdsaliency's Issues

Implementation Details

Thanks for sharing the code!
I would like to reimplement the same in PyTorch.

Could you give some more insights on how to start off or perhaps give some comments on what each file does or some tips in general. I'm a little new to MATLAB but I really liked the paper and would like to investigate this area.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.