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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Salient Object Detection in the Deep Learning Era: An In-Depth Survey
| Date | Stars |
|---|---|
| 2026-07-31 | 394 |
| 2026-08-04 | 394 |
| 2026-08-06 | 394 |
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# [Salient object detection in the deep learning era: An in-depth survey, PAMI2021](https://www.researchgate.net/publication/332553805_Salient_Object_Detection_in_the_Deep_Learning_Era_An_In-Depth_Survey)
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[Wenguan Wang](https://sites.google.com/view/wenguanwang), 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).
<img src="result2.png" width="800px"/>
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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]
[email protected]
Excerpt of 2,756 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:18b0a7a2a79de90c, llm:Repository description and topics: 'Salient Object Detection in the Deep Learning Era: An In-Depth Survey'; topics include saliency, saliency-maps, saliency-prediction, salient-object-detection, sod; README provides saliency prediction maps, dataset annotations, evaluation code.
matched fp:18b0a7a2a79de90c, llm:Repository description and topics: 'Salient Object Detection in the Deep Learning Era: An In-Depth Survey'; topics include saliency, saliency-maps, saliency-prediction, salient-object-detection, sod; README provides saliency prediction maps, dataset annotations, evaluation code.
matched fp:18b0a7a2a79de90c, llm:Repository description and topics: 'Salient Object Detection in the Deep Learning Era: An In-Depth Survey'; topics include saliency, saliency-maps, saliency-prediction, salient-object-detection, sod; README provides saliency prediction maps, dataset annotations, evaluation code.