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<p align="center"> <img src="./Image/DANet_logo.png" alt="Logo" width="210" height="auto"> <h3 align="center">A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection</h3> <p align="center"> Xiaoqi Zhao, Lihe Zhang, Youwei Pang, Huchuan Lu, Lei Zhang <br /> <a href="https://arxiv.org/pdf/2007.06811.pdf"><strong>⭐ arXiv »</strong></a> <br /> </p> </p>

The official repo of the ECCV 2020 paper A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection.

Saliency map

Google Drive / BaiduYunPan(3m9i)

Related Works

(ECCV 2020 Oral) Suppress and Balance: A Simple Gated Network for Salient Object Detection: https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
(ECCV 2020) Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection: https://github.com/lartpang/HDFNet
(CVPR 2020) Multi-scale Interactive Network for Salient Object Detection: https://github.com/lartpang/MINet

Network

Module

Quantitative comparison

Visual comparison

Trained Model

You can download the trained VGG16-model(DUT-RGBD or NJUD&NLPR) at BaiduYunPan(5uhd).

Requirement

Python 3.7
PyTorch 1.5.0
torchvision
numpy
Pillow
Cython

Training

1.Set the path of training sets in config.py
2.Run train.py

Testing

1.Set the path of testing sets in config.py
2.Run generate_salmap.py (can generate the predicted saliency maps)
3.Run generate_visfeamaps.py (can visualize feature maps)
4.Run test_metric_score.py (can evaluate the predicted saliency maps in terms of fmax,fmean,wfm,sm,em,mae). You also can use the toolkit released by us:https://github.com/lartpang/Py-SOD-VOS-EvalToolkit.

BibTex

@inproceedings{DANet,
  title={A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection},
  author={Zhao, Xiaoqi and Zhang, Lihe and Pang, Youwei and Lu, Huchuan and Zhang, Lei},
  booktitle=ECCV,
  year={2020}
}