A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection

Introduction

Accepted paper in CVPR2020, 'A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection', Yongri Piao, Zhengkun Rong, Miao Zhang, Weisong Ren 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

Training and Testing Datasets

Training dataset

• Download Link. Code: 0fj8

Testing dataset

• Download Link. Code: f7vk

Depth Stream

Training

• Modify your path of training dataset in train_depth
• Run train_depth

Testing

• Download pretrained depth model from here. Code: uklr
• Modify your path of testing dataset in test_depth
• Run test_depth to inference saliency maps
• Saliency maps generated from the depth stream can be downnloaded from here. Code: 2e3l

RGB Stream

Training

• Modify your path of training dataset in train_RGB
• Modify the pretrained depth model path
• Run train_RGB

Testing

• Download pretrained RGB model from here. Code: tj7b
• Modify your path of testing dataset in test_depth
• Run test_RGB to inference saliency maps
• Saliency maps generated from the RGB stream can be downnloaded from here. Code: tb3y

Contact and Questions

Contact: Zhengkun Rong. Email: [email protected] or [email protected]