GilbertRC / AAAI2020-Exploit-and-Replace-Light-Field-Saliency

Exploit and Replace: An Asymmetrical Two-Stream Architecture for Versatile Light Field Saliency Detection

Introduction

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.

Usage Instructions

Requirements

• Windows 10
• PyTorch 0.4.1
• CUDA 9.0
• Cudnn 7.6.0
• Python 3.6.5
• Numpy 1.16.4

Exploit -- Focal Stream (Teacher)

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

Replace -- RGB Stream (Student)

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

Saliency Maps

Focal Stream (Teacher)

• Download Link. Code: 58to

RGB Stream (Student)

• Download Link. Code: nfqs

Contact and Questions

Contact: Zhengkun Rong. Email: [e-mail] or [e-mail]