Exploit and Replace: An Asymmetrical Two-Stream Architecture for Versatile Light Field Saliency Detection
Accepted paper in AAAI2020, 'Exploit and Replace: An Asymmetrical Two-Stream Architecture for Versatile Light Field Saliency Detection', Yongri Piao, Zhengkun Rong, Miao Zhang and Huchuan Lu.
Requirements
- Windows 10
- PyTorch 0.4.1
- CUDA 9.0
- Cudnn 7.6.0
- Python 3.6.5
- Numpy 1.16.4
Training
- Modify your path of training dataset in Demo_Teacher
- Set args.phase = train
- Set args.param = False
- Run Demo_Teacher
Testing
- Download pretrained focal model from here. Code: vee3
- Modify your path of testing dataset in Demo_Teacher
- Set args.phase = test
- Set args.param = True
- Run Demo_Teacher to inference saliency maps
Training
- Modify your path of training dataset in Demo_Student
- Set args.phase = train
- Set args.param = False
- Run Demo_Student
Testing
- Download pretrained RGB model (comming soon)
- Modify your path of testing dataset in Demo_Student
- Set args.phase = test
- Set args.param = True
- Run Demo_Student to inference saliency maps
Focal Stream (Teacher)
- Download Link. Code: 58to
RGB Stream (Student)
- Download Link. Code: nfqs
Contact: Zhengkun Rong. Email: 18642840242@163.com or rzk911113@mail.dlut.edu.cn