Skip to content

DUT-IIAU-OIP-Lab/IJCAI2019-Deep-Light-Field-Driven-Saliency-Detection-from-A-Single-View

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

45 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep-Light-Field-Driven-Saliency-Detection-from-A-Single-View

Introduction

Accepted paper in IJCAI2019, 'Deep Light-Field-Driven Saliency Detection from A Single View', Yongri Piao, Zhengkun Rong, Miao Zhang, Xiao Li and Huchuan Lu.

Dataset: DUTLF-V1

  • This dataset consists of DUTLF-MV, DUTLF-FS and DUTLF-Depth.
  • The dataset will be expanded to 3000 or so real scenes.
  • We are working on it and will make it publicly available soon.

Dataset: DUTLF-MV

  • DUTLF-MV is part of DUTLF, which consists of 1580 real scenes.
  • Each scene of this dataset consists of an all-focus image, multi-view images and a corresponding ground truth.
  • Dataset can be downloaded from here. Code: 9c7k
  • Training set: 1100 samples before data augmentation
  • Testing set: 480 samples

Usage Instructions

Requirements

  • Windows 10
  • Tensorflow 1.10.0
  • CUDA 9.0
  • Cudnn 9.0
  • Python 3.6.5
  • Numpy 1.14.3

Training

  • Download pretrained vgg-19.npy from here. Code: yiov
  • Hyperparameter: is_training=1
  • Modify your path of training dataset
  • Run Main_model
  • cd 'your path'/logs, tensorboard --logdir=train

Testing

  • Download pretrained model from here. Code: eu72
  • Hyperparameter: is_training=0
  • Modify your path of testing dataset
  • Run Main_model to generate saliency maps, synthesized mutli-view images and depth maps

Saliency map

Saliency maps of this paper can be downloaded BaiduYun. Code: 2jl0

Contact and Questions

Contact: Zhengkun Rong. Email: 18642840242@163.com or rzk911113@mail.dlut.edu.cn

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%