Salient object detection in the deep learning era: An in-depth survey, PAMI2021

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Wenguan Wang, Qiuxia Lai, Huazhu Fu, Jianbing Shen, Haibin Ling, Ruigang Yang

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It is very welcome to send me your saliency maps if your work is published in top-level conference.

If I miss your work, please let me know and I'll add it.

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Google Disk: https://drive.google.com/open?id=1WSmPaUV909uWF3ycL0MLWPWM6MdSjaJ0

Baidu Disk: https://pan.baidu.com/s/1f63o_QV4za6cdcigHSwhWw extraction code：jp53

Here include the saliency prediction maps for 46 major deep salient object detection (SOD) methods, a constructed dataset with annotations for attribute analysis, and codes for evaluation (see our paper for details).

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:fire::fire::fire:Update

2020/1: Results of eight ICCV'19 methods are added.

2019/9: Results of eight CVPR'19 methods are added.

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1. Saliency prediction maps DUT.rar (DUT-OMRON dataset) DUTSTE.rar (test set of DUTS dataset) ECSSD.rar (ECSSD dataset) HKU-IS.rar (HKU-IS dataset) PASCAL-S.rar (PASCAL-S dataset) SOD.rar (SOD dataset)

2. Dataset and annotations for attribute analysis The hybrid dataset consists of 1,800 images randomly selected from 6 datasets, namely SOD, ECSSD, DUT-OMRON, PASCAL-S, HKU-IS and the test set of DUTS (300 for each). We carefully exclude images in ECSSD that also appear in SOD.

The annotations listed in ATTR_anno.xlsx cover 16 attributes from the perspectives of salient object categories, challenges and scene categories.

3. Codes for evaluation Matlab codes for calculating F-max, S-measure and MAE.

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Citation:

@article{wang2019sodsurvey,
	title={Salient Object Detection in the Deep Learning Era: An In-Depth Survey},
	author={Wang, Wenguan and Lai, Qiuxia and Fu, Huazhu and Shen, Jianbing and Ling, Haibin and Yang, Ruigang},
	journal={TPAMI},
	year={2021},
}

If you find our dataset is useful, please cite above paper.

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Contact Information

Email:

[email protected]

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