Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency

Suppress and Balance: A Simple Gated Network for Salient Object Detection

Xiaoqi Zhao*, Youwei Pang*, Lihe Zhang, Huchuan Lu, Lei Zhang
⭐ arXiv » [Slides]

The official repo of the ECCV 2020 (oral) paper, Suppress and Balance: A Simple Gated Network for Salient Object Detection.

Results(RGB/RGB-D SOD, VOS)

RGB SOD: Google Drive / BaiduYunPan(4ha3) RGB-D SOD: Google Drive / BaiduYunPan(9her) VOS: Google Drive / BaiduYunPan(0hue)

Related Works

(ECCV 2020) A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection: https://github.com/Xiaoqi-Zhao-DLUT/DANet-RGBD-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

Trained Model

You can download the trained VGG-16/ResNet-50 GateNet model at BaiduYunPan(s3l2).

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
We also release the new baseline, Gated FPN in [model_GatedFPN_newbaseline.py]. You can use it for future reserach.

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.

We also recommend you to use another code released by Jun Wei https://github.com/weijun88/F3Net. Based on the training strategy of this code, our performance can be further improved( but for fair comparisons, we do not use the multi-scale training trick), the performance as follows:
(ResNet-50-GateNet)
dataset: DUTS MAE: 0.0375 maxF: 0.8899 avgF: 0.8199 wfm: 0.8225 Sm: 0.8889 Em: 0.9133
dataset: HKU-IS MAE: 0.0317 maxF: 0.9353 avgF: 0.9010 wfm: 0.8873 Sm: 0.9187 Em: 0.9558
dataset: ECSSD MAE: 0.0355 maxF: 0.9486 avgF: 0.9217 wfm: 0.9049 Sm: 0.9269 Em: 0.9513
dataset: DUT-OMRON MAE: 0.0536 maxF: 0.8157 avgF: 0.7497 wfm: 0.7365 Sm: 0.8386 Em: 0.8697
dataset: PASCAL-S MAE: 0.0648 maxF: 0.8882 avgF: 0.8400 wfm: 0.8213 Sm: 0.8625 Em: 0.8958
In addition, this performance is based on a model that reduces the number of output channels of Fold-ASPP by half, so the entire parameter and model size can be greatly reduced. We believe that Fold-ASPP can still be optimized in the future.

BibTex

@inproceedings{GateNet,
  title={Suppress and Balance: A Simple Gated Network for Salient Object Detection},
  author={Zhao, Xiaoqi and Pang, Youwei and Zhang, Lihe and Lu, Huchuan and Zhang, Lei},
  booktitle=ECCV,
  year={2020}
}

Xiaoqi-Zhao-DLUT / GateNet-RGB-Saliency