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