CVPR2020-A2dele

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CVPR2020-A2dele

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# 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](http://ice.dlut.edu.cn/yrpiao/), Zhengkun Rong, Miao Zhang, Weisong Ren and [Huchuan Lu](http://ice.dlut.edu.cn/lu/publications.html).

## 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](https://pan.baidu.com/s/14cGEwcCRulWDOuKNIjuGCg). Code: 0fj8

Testing dataset
* [Download Link](https://pan.baidu.com/s/1Yp5YtVIBB3-9PMFruYhxSw). Code: f7vk

## Depth Stream
Training
* Modify your path of training dataset in train_depth
* Run train_depth

Testing
* Download pretrained depth model from [here](https://pan.baidu.com/s/1zM25yQ_Q-LgE4yOK-JJaTQ). 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](https://pan.baidu.com/s/121a4Pn7SkOijlXGHqUjApA). 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](https://pan.baidu.com/s/1BNZKsmryBpSbCczYDPM-rA). 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](https://pan.baidu.com/s/1egZM__Xs9BmnxxaVyiB4ug). Code: tb3y

## Contact and Questions
Contact: Zhengkun Rong. Email: 18642840242@163.com or rzk911113@mail.dlut.edu.cn

CVPR2020-A2dele

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