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: 18642840242@163.com or rzk911113@mail.dlut.edu.cn