It is a implementation based on the following paper and the repo for the 2018 ICME Salient 360 challenge:

• IEEE ICME Salient360! Grand Challenge: Visual attention modeling for 360 Content - 2018 edition.
  https://salient360.ls2n.fr

• Kao Zhang, Zhenzhong Chen. Video Saliency Prediction Based on Spatial-Temporal Two-Stream Network. IEEE Trans. Circuits Syst. Video Techn. 2018.
  Github: https://github.com/zhangkao/IIP_TwoS_Saliency

• My test environment:
  

    windows10, python3.6, keras2.1, tensorflow1.6, cuda9.0
  

• My device:

    GTX1080, 8G memory, Intel E5-CPU 32G RAM
  

How to run our code for task 1 and 2 ?

• please change the working directory: "wkdir" to your path in the "zk_config.py" file, like

     "wkdir = 'E:/Salient360-2018/Task1_2'" ;
  

• set the parameter "task_type = 'H'" or "task_type = 'HE'" for these two tasks in "zk_config.py" file;

• set the parameter "with_CB = True"(default) or "with_CB = False" to control whether to use the center bias method or not;

• put the test stimuli to "DataSet/Images/Stimuli" and "DataSet/Videos/Stimuli" folders in the "wkdir" path;

• then run the demo "Test_images_Demo.py" and "Test_videos_Demo.py".

• The results of image task is saved by ".bin"(float32) and ".png" formats.
• The results of video task is saved by ".bin"(float32), ".mat"(int32) and ".mp4" formats.
• And it is easy to change the output format in our code.

If you use the video saliency model, please cite the following paper:

@article{Zhang2018Video,
  author  = {Kao Zhang and Zhenzhong Chen},
  title   = {Video Saliency Prediction Based on Spatial-Temporal Two-Stream Network},
  journal = {IEEE Transactions on Circuits and Systems for Video Technology },
  year    = {2018}
}

Kao ZHANG
Laboratory of Intelligent Information Processing (LabIIP)
Wuhan University, Wuhan, China.
Email: zhangkao@whu.edu.cn

Zhenzhong CHEN (Professor and Director)
Laboratory of Intelligent Information Processing (LabIIP)
Wuhan University, Wuhan, China.
Email: zzchen@whu.edu.cn
Web: http://iip.whu.edu.cn/~zzchen/