To use this toolbox, read README.md in folder 'tools'.
Datasets [citations]
- ECSSD
link
- PASCAL-S
link
- DUT-OMRON
link
- HKU-IS
link
- DUTS
link
- SOD
link
- SOS
link
- THUR
link
- MSRA10K
link
- SED
link
Methods [citations]
Only fully supervised Deep Learning based methods are included.
NOTE: please see my another repo to get the papers.
2019
Method Name | AFNet | PoolNet | BASNet | CPD | MLMSNet |
---|---|---|---|---|---|
Platform | Caffe | PyTorch | PyTorch | PyTorch | PyTorch |
Model Size | 143.9 MB | 278.5 MB (ResNet50) | 348.5 MB (ResNet34) | 192.0 MB (ResNet50) | 297.6 MB |
2018
Method Name | BMPM | DGRL | PAGR | RAS | PiCANet | R3Net |
---|---|---|---|---|---|---|
Platform | Tensorflow | Caffe | Caffe | Caffe | Caffe | PyTorch |
Model Size | - | 648.0 MB (ResNet50) | - | 81.0 MB | 153.3/197.2 MB (VGG/ResNet50) | 225.3 MB (ResNeXt101) |
2017
Method Name | Amulet | UCF | SRM | MSRNet | NLDF | DSS |
---|---|---|---|---|---|---|
Platform | Caffe | Caffe | Caffe | Caffe | Tensorflow | Caffe |
Model Size | 132.6 MB | 117.9 MB | 213.1 MB (ResNet50) | 331.8 MB | 425.9 MB | 447.3 MB |
2016
Method Name | RFCN | SCSD-HS | DS | ELD | DCL | DHS |
---|---|---|---|---|---|---|
Platform | Caffe | Caffe | Caffe | Caffe | Caffe | Caffe |
Model Size | 1126.4 MB | - | 537.1 MB | 667.2 MB | 265.0 MB | 376.2 MB |
2015
Method Name | LEGS | MCDL | MDF |
---|---|---|---|
Platform | Caffe | Caffe | Caffe |
Model Size | 73.6 MB | 233.1 MB | 330.8 MB |
All saliency maps are provided by the authors or calculated using their released codes.
pre-computed saliency maps [BaiduYun]
pre-computed
.mat
files [BaiduYun] (please contact me if you need this)
Methods | year | max F-measure | mean F-measure | MAE | S-measure | IoU(@ max Fm) | mean IoU | max IoU |
---|---|---|---|---|---|---|---|---|
MLMSNet | 2019 | .928 | .868 | .045 | .911 | .838 | .821 | .854 |
CPD-R | 2019 | .939 | .917 | .037 | .918 | .849 | .847 | .865 |
BASNet | 2019 | .942 | .879 | .037 | .916 | .860 | .855 | .870 |
PoolNet | 2019 | .949 | .918 | .035 | .926 | .862 | .858 | .879 |
AFNet | 2019 | .935 | .908 | .042 | .914 | .839 | .835 | .857 |
BMPM | 2018 | .929 | .869 | .045 | .911 | .838 | .821 | .854 |
DGRL | 2018 | .922 | .906 | .041 | .903 | .830 | .838 | .846 |
PAGR | 2018 | .927 | .894 | .061 | .889 | .806 | .770 | .825 |
RAS | 2018 | .921 | .889 | .056 | .893 | .808 | .792 | .823 |
PiCANet | 2018 | .931 | .884 | .047 | .914 | .827 | .812 | .853 |
PiCANet-C | 2018 | .932 | .913 | .036 | .910 | .844 | .850 | .858 |
R3Net | 2018 | .931 | .917 | .046 | .900 | .831 | .825 | .850 |
Amulet | 2017 | .915 | .870 | .059 | .894 | .800 | .787 | .822 |
UCF | 2017 | .911 | .840 | .078 | .883 | .785 | .745 | .812 |
SRM | 2017 | .917 | .892 | .054 | .895 | .796 | .783 | .824 |
MSRNet | 2017 | .911 | .839 | .054 | .896 | .790 | .791 | .820 |
NLDF | 2017 | .905 | .878 | .063 | .875 | .773 | .766 | .798 |
DSS | 2017 | .916 | .901 | .052 | .882 | .796 | .803 | .816 |
RFCN | 2016 | .890 | .834 | .107 | .852 | .740 | .645 | .763 |
SCSD-HS | 2016 | .865 | .719 | .192 | .773 | .707 | .569 | .745 |
DS | 2016 | .882 | .826 | .122 | .821 | .726 | .552 | .755 |
ELD | 2016 | .867 | .810 | .079 | .839 | .699 | .709 | .727 |
DCL | 2016 | .890 | .829 | .088 | .828 | .748 | .646 | .777 |
DHS | 2016 | .907 | .872 | .059 | .884 | .779 | .773 | .805 |
LEGS | 2015 | .827 | .785 | .118 | .787 | .656 | .574 | .678 |
MCDL | 2015 | .837 | .796 | .101 | .803 | .656 | .615 | .688 |
MDF | 2015 | .832 | .807 | .105 | .776 | .641 | .599 | .682 |
pre-computed saliency maps
[BaiduYun] Fetch Code: rv6h[Google Drive]
IT'S WEIRD!!!!!THE LINK WILL BE INVALID A FEW DAYS AFTER I UPDATED IT!!!!!
pre-computed
.mat
files [BaiduYun] (please contact me if you need this)
Methods | year | max F-measure | mean F-measure | MAE | S-measure | IoU(@ max Fm) | mean IoU | max IoU |
---|---|---|---|---|---|---|---|---|
MLMSNet | 2019 | .862 | .769 | .074 | .845 | .732 | .728 | .753 |
CPD-R | 2019 | .869 | .829 | .072 | .847 | .726 | .743 | .757 |
BASNet | 2019 | .862 | .779 | .077 | .837 | .733 | .740 | .749 |
PoolNet | 2019 | .890 | .837 | .065 | .866 | .763 | .768 | .784 |
AFNet | 2019 | .868 | .826 | .071 | .850 | .736 | .743 | .760 |
BMPM | 2018 | .862 | .769 | .074 | .845 | .732 | .728 | .753 |
DGRL | 2018 | .854 | .825 | .072 | .836 | .736 | .742 | .747 |
PAGR | 2018 | .856 | .807 | .093 | .818 | .690 | .664 | .713 |
RAS | 2018 | .837 | .785 | .104 | .795 | .658 | .654 | .676 |
PiCANet | 2018 | .868 | .801 | .077 | .850 | .732 | .725 | .760 |
PiCANet-C | 2018 | .867 | .833 | .067 | .843 | .736 | .757 | .763 |
R3Net | 2018 | .845 | .807 | .097 | .800 | .675 | .677 | .697 |
Amulet | 2017 | .837 | .768 | .098 | .820 | .690 | .687 | .717 |
UCF | 2017 | .828 | .706 | .126 | .803 | .664 | .639 | .695 |
SRM | 2017 | .847 | .801 | .085 | .832 | .695 | .688 | .724 |
MSRNet | 2017 | .855 | .744 | .081 | .840 | .699 | .707 | .734 |
NLDF | 2017 | .831 | .779 | .099 | .803 | .653 | .664 | .686 |
DSS | 2017 | .836 | .804 | .096 | .797 | .666 | .676 | .687 |
RFCN | 2016 | .837 | .751 | .118 | .808 | .649 | .587 | .674 |
SCSD-HS | 2016 | .779 | .589 | .220 | .715 | .584 | .490 | .624 |
DS | 2016 | .765 | .659 | .176 | .739 | .564 | .451 | .614 |
ELD | 2016 | .773 | .718 | .123 | .757 | .558 | .586 | .605 |
DCL | 2016 | .805 | .714 | .125 | .754 | .626 | .558 | .665 |
DHS | 2016 | .829 | .779 | .094 | .807 | .659 | .660 | .688 |
LEGS | 2015 | .762 | .704 | .155 | .725 | .544 | .493 | .588 |
MCDL | 2015 | .743 | .691 | .145 | .719 | .533 | .497 | .565 |
MDF | 2015 | .768 | .709 | .146 | .692 | .541 | .479 | .585 |
pre-computed saliency maps [BaiduYun]
pre-computed
.mat
files [BaiduYun] (please contact me if you need this)
Methods | year | max F-measure | mean F-measure | MAE | S-measure | IoU(@ max Fm) | mean IoU | max IoU |
---|---|---|---|---|---|---|---|---|
MLMSNet | 2019 | .774 | .692 | .064 | .809 | .632 | .627 | .654 |
CPD-R | 2019 | .796 | .747 | .056 | .824 | .656 | .659 | .681 |
BASNet | 2019 | .805 | .755 | .057 | .836 | .682 | .699 | .711 |
PoolNet | 2019 | .805 | .752 | .054 | .831 | .670 | .669 | .692 |
AFNet | 2019 | .797 | .738 | .057 | .826 | .653 | .660 | .682 |
BMPM | 2018 | .774 | .692 | .064 | .809 | .632 | .627 | .654 |
DGRL | 2018 | .774 | .733 | .062 | .806 | .640 | .649 | .657 |
PAGR | 2018 | .771 | .711 | .071 | .775 | .586 | .555 | .611 |
RAS | 2018 | .786 | .713 | .062 | .814 | .638 | .633 | .660 |
PiCANet | 2018 | .794 | .710 | .068 | .826 | .657 | .640 | .682 |
PiCANet-C | 2018 | .784 | .751 | .057 | .815 | .647 | .668 | .675 |
R3Net | 2018 | .792 | .756 | .061 | .815 | .642 | .661 | .674 |
Amulet | 2017 | .742 | .647 | .098 | .780 | .594 | .589 | .622 |
UCF | 2017 | .734 | .613 | .132 | .758 | .580 | .545 | .608 |
SRM | 2017 | .769 | .707 | .069 | .797 | .605 | .585 | .634 |
MSRNet | 2017 | .782 | .676 | .073 | .808 | .616 | .618 | .648 |
NLDF | 2017 | .753 | .684 | .080 | .770 | .562 | .562 | .593 |
DSS | 2017 | .771 | .729 | .066 | .788 | .605 | .617 | .629 |
RFCN | 2016 | .742 | .627 | .111 | .774 | .553 | .492 | .583 |
SCSD-HS | 2016 | .754 | .592 | .194 | .693 | .591 | .466 | .611 |
DS | 2016 | .745 | .603 | .120 | .750 | .551 | .451 | .585 |
ELD | 2016 | .715 | .611 | .092 | .750 | .528 | .540 | .561 |
DCL | 2016 | .739 | .684 | .097 | .713 | .553 | .482 | .584 |
DHS | 2016 | -- | -- | -- | -- | -- | -- | -- |
LEGS | 2015 | .669 | .592 | .133 | .714 | .493 | .454 | .512 |
MCDL | 2015 | .701 | .625 | .089 | .752 | .541 | .512 | .558 |
MDF | 2015 | .694 | .644 | .092 | .721 | .490 | .475 | .526 |
pre-computed saliency maps [BaiduYun]
pre-computed
.mat
files [BaiduYun] (please contact me if you need this)
Methods | year | max F-measure | mean F-measure | MAE | S-measure | IoU(@ max Fm) | mean IoU | max IoU |
---|---|---|---|---|---|---|---|---|
MLMSNet | 2019 | .921 | .871 | .039 | .906 | .818 | .801 | .838 |
CPD-R | 2019 | .927 | .894 | .033 | .908 | .819 | .817 | .839 |
BASNet | 2019 | .928 | .896 | .032 | .909 | .839 | .832 | .848 |
PoolNet | 2019 | .936 | .903 | .030 | .919 | .840 | .834 | .858 |
AFNet | 2019 | .923 | .888 | .036 | .905 | .814 | .809 | .835 |
BMPM | 2018 | .921 | .871 | .039 | .907 | .818 | .801 | .838 |
DGRL | 2018 | .910 | .890 | .036 | .895 | .802 | .811 | .820 |
PAGR | 2018 | .918 | .886 | .048 | .887 | .791 | .753 | .814 |
RAS | 2018 | .913 | .871 | .045 | .887 | .788 | .771 | .807 |
PiCANet | 2018 | .921 | .870 | .042 | .906 | .809 | .786 | .833 |
PiCANet-C | 2018 | .925 | .907 | .031 | .904 | .820 | .833 | .841 |
R3Net | 2018 | .917 | .905 | .038 | .891 | .799 | .801 | .824 |
Amulet | 2017 | .895 | .839 | .052 | .883 | .772 | .755 | .797 |
UCF | 2017 | .886 | .808 | .074 | .866 | .747 | .706 | .777 |
SRM | 2017 | .906 | .874 | .046 | .887 | .772 | .754 | .803 |
MSRNet | 2017 | .923 | .868 | .036 | .912 | .809 | .803 | .838 |
NLDF | 2017 | .902 | .874 | .048 | .879 | .770 | .761 | .795 |
DSS | 2017 | .910 | .895 | .041 | .879 | .779 | .788 | .805 |
RFCN | 2016 | .892 | .835 | .079 | .858 | .746 | .643 | .772 |
SCSD-HS | 2016 | .871 | .740 | .177 | .760 | .716 | .544 | .744 |
DS | 2016 | .865 | .788 | .080 | .852 | .696 | .645 | .737 |
ELD | 2016 | .839 | .769 | .074 | .820 | .652 | .668 | .689 |
DCL | 2016 | .885 | .853 | .072 | .819 | .729 | .623 | .763 |
DHS | 2016 | .890 | .855 | .053 | .870 | .746 | .735 | .774 |
LEGS | 2015 | .766 | .723 | .119 | .742 | .557 | .499 | .599 |
MCDL | 2015 | .808 | .757 | .092 | .786 | .623 | .572 | .647 |
MDF | 2015 | .861 | .784 | .129 | .810 | .688 | .541 | .718 |
pre-computed saliency maps [BaiduYun]
pre-computed
.mat
files [BaiduYun] (please contact me if you need this)
Methods | year | max F-measure | mean F-measure | MAE | S-measure | IoU(@ max Fm) | mean IoU | max IoU |
---|---|---|---|---|---|---|---|---|
MLMSNet | 2019 | .851 | .751 | .049 | .861 | .706 | .698 | .736 |
CPD-R | 2019 | .866 | .810 | .044 | .869 | .724 | .725 | .752 |
BASNet | 2019 | .859 | .796 | .048 | .865 | .742 | .741 | .756 |
PoolNet | 2019 | .889 | .825 | .037 | .886 | .759 | .754 | .783 |
AFNet | 2019 | .862 | .797 | .046 | .866 | .721 | .719 | .748 |
BMPM | 2018 | .851 | .751 | .049 | .861 | .706 | .698 | .736 |
DGRL | 2018 | .829 | .798 | .050 | .841 | .692 | .703 | .713 |
PAGR | 2018 | .855 | .788 | .056 | .837 | .685 | .642 | .713 |
RAS | 2018 | .831 | .755 | .060 | .839 | .675 | .667 | .697 |
PiCANet | 2018 | .851 | .755 | .054 | .861 | .700 | .685 | .735 |
PiCANet-C | 2018 | .850 | .818 | .046 | .850 | .702 | .722 | .734 |
R3Net | 2018 | .828 | .796 | .059 | .829 | .665 | .678 | .598 |
Amulet | 2017 | .778 | .676 | .085 | .803 | .615 | .609 | .646 |
UCF | 2017 | .771 | .629 | .117 | .778 | .598 | .562 | .628 |
SRM | 2017 | .827 | .757 | .059 | .834 | .657 | .638 | .690 |
MSRNet | 2017 | .829 | .708 | .061 | .840 | .654 | .658 | .692 |
NLDF | 2017 | .812 | .743 | .066 | .815 | .624 | .631 | .661 |
DSS | 2017 | .825 | .791 | .057 | .822 | .652 | .670 | .684 |
RFCN | 2016 | .784 | .712 | .091 | .792 | .608 | .540 | .633 |
SCSD-HS | 2016 | |||||||
DS | 2016 | .777 | .633 | .090 | .793 | .577 | .532 | .617 |
ELD | 2016 | .738 | .628 | .093 | .753 | .528 | .541 | .561 |
DCL | 2016 | .782 | .714 | .088 | .735 | .589 | .504 | .625 |
DHS | 2016 | .807 | .724 | .067 | .817 | .621 | .621 | .660 |
LEGS | 2015 | .655 | .585 | .138 | .694 | .454 | .423 | .485 |
MCDL | 2015 | .672 | .594 | .106 | .712 | .469 | .442 | .493 |
MDF | 2015 | .730 | .673 | .094 | .732 | .504 | .485 | .543 |
pre-computed saliency maps [BaiduYun]
pre-computed
.mat
files [BaiduYun] (please contact me if you need this)
Methods | year | max F-measure | mean F-measure | MAE | S-measure | IoU(@ max Fm) | mean IoU | max IoU |
---|---|---|---|---|---|---|---|---|
MLMSNet | 2019 | .855 | .763 | .107 | .787 | .675 | .633 | .692 |
CPD-R | 2019 | .860 | .810 | .111 | .768 | .670 | .616 | .691 |
BASNet | 2019 | .851 | .745 | .113 | .769 | .662 | .635 | .673 |
PoolNet | 2019 | .875 | .830 | .104 | .788 | .708 | .652 | .725 |
AFNet | 2019 | .856 | .809 | .109 | .777 | .670 | .626 | .693 |
BMPM | 2018 | .855 | .763 | .107 | .787 | .675 | .633 | .692 |
DGRL | 2018 | .845 | .799 | .104 | .771 | .655 | .642 | .668 |
PAGR | 2018 | |||||||
RAS | 2018 | .850 | .799 | .124 | .764 | .644 | .611 | .661 |
PiCANet | 2018 | .853 | .791 | .102 | .791 | .679 | .628 | .701 |
PiCANet-C | 2018 | .836 | .800 | .087 | .772 | .669 | .638 | .680 |
R3Net | 2018 | .836 | .789 | .136 | .732 | .600 | .573 | .600 |
Amulet | 2017 | .806 | .755 | .141 | .758 | .619 | .596 | .642 |
UCF | 2017 | .803 | .699 | .164 | .754 | .601 | .566 | .634 |
SRM | 2017 | .843 | .800 | .127 | .742 | .636 | .562 | .665 |
MSRNet | 2017 | .836 | .741 | .113 | .779 | .653 | .614 | .683 |
NLDF | 2017 | .841 | .791 | .124 | .757 | .654 | .599 | .678 |
DSS | 2017 | .844 | .795 | .121 | .751 | .651 | .608 | .656 |
RFCN | 2016 | .799 | .751 | .170 | .730 | .602 | .488 | .629 |
SCSD-HS | 2016 | .796 | .628 | .222 | .710 | .592 | .477 | .612 |
DS | 2016 | .784 | .698 | .190 | .712 | .566 | .427 | .603 |
ELD | 2016 | .764 | .712 | .155 | .705 | .534 | .524 | .563 |
DCL | 2016 | .823 | .741 | .141 | .735 | .624 | .506 | .653 |
DHS | 2016 | .827 | .774 | .128 | .750 | .628 | .578 | .658 |
LEGS | 2015 | .734 | .683 | .196 | .657 | .495 | .430 | .533 |
MCDL | 2015 | .731 | .677 | .181 | .650 | .505 | .417 | .528 |
MDF | 2015 | .787 | .721 | .159 | .679 | .563 | .460 | .585 |
% ECSSD
@inproceedings{yan2013hierarchical,
title={Hierarchical saliency detection},
author={Yan, Qiong and Xu, Li and Shi, Jianping and Jia, Jiaya},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={1155--1162},
year={2013}
}
% PASCAL-S
@inproceedings{li2014secrets,
title={The secrets of salient object segmentation},
author={Li, Yin and Hou, Xiaodi and Koch, Christof and Rehg, James M and Yuille, Alan L},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={280--287},
year={2014}
}
% DUT-OMRON
@inproceedings{yang2013saliency,
title={Saliency detection via graph-based manifold ranking},
author={Yang, Chuan and Zhang, Lihe and Lu, Huchuan and Ruan, Xiang and Yang, Ming-Hsuan},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={3166--3173},
year={2013}
}
% HKU-IS
@inproceedings{li2015visual,
title={Visual saliency based on multiscale deep features},
author={Li, Guanbin and Yu, Yizhou},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5455--5463},
year={2015}
}
% DUTS
@inproceedings{wang2017,
title={Learning to Detect Salient Objects with Image-level Supervision},
author={Wang, Lijun and Lu, Huchuan and Wang, Yifan and Feng, Mengyang and Wang, Dong, and Yin, Baocai and Ruan, Xiang},
booktitle={CVPR},
year={2017}
}
% SOD
@inproceedings{movahedi2010design,
title={Design and perceptual validation of performance measures for salient object segmentation},
author={Movahedi, Vida and Elder, James H},
booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on},
pages={49--56},
year={2010},
organization={IEEE}
}
% SOS
@inproceedings{zhang2015salient,
title={Salient object subitizing},
author={Zhang, Jianming and Ma, Shugao and Sameki, Mehrnoosh and Sclaroff, Stan and Betke, Margrit and Lin, Zhe and Shen, Xiaohui and Price, Brian and Mech, Radomir},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={4045--4054},
year={2015}
}
% THUR
@article{cheng2014salientshape,
title={Salientshape: Group saliency in image collections},
author={Cheng, Ming-Ming and Mitra, Niloy J and Huang, Xiaolei and Hu, Shi-Min},
journal={The Visual Computer},
volume={30},
number={4},
pages={443--453},
year={2014},
publisher={Springer}
}
% MSRA10K
@article{ChengPAMI,
author = {Ming-Ming Cheng and Niloy J. Mitra and Xiaolei Huang and Philip H. S. Torr and Shi-Min Hu},
title = {Global Contrast based Salient Region Detection},
year = {2015},
journal= {IEEE TPAMI},
volume={37},
number={3},
pages={569--582},
doi = {10.1109/TPAMI.2014.2345401},
}
% SED
@inproceedings{alpert2007image,
title={Image segmentation by probabilistic bottom-up aggregation and cue integration},
author={Alpert, Sharon and Galun, Meirav and Basri, Ronen and Brandt, Achi},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={1--8},
year={2007},
organization={IEEE}
}
%MLMSNet
@InProceedings{WuRunMin_2019_CVPR,
author = {Wu, Runmin and Feng, Mengyang and Guan, Wenlong and Wang, Dong and Lu, Huchuan and Ding, Errui},
title = {A Mutual Learning Method for Salient Object Detection with intertwined Multi-Supervision},
booktitle = CVPR,
year = {2019}
}
%CPD
@InProceedings{Wu_2019_CVPR,
author = {Wu, Zhe and Su, Li and Huang, Qingming},
title = {Cascaded Partial Decoder for Fast and Accurate Salient Object Detection},
booktitle = CVPR,
year = {2019}
}
%BASNet
@InProceedings{Qin_2019_CVPR,
author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Gao, Chao and Dehghan, Masood and Jagersand, Martin},
title = {BASNet: Boundary-Aware Salient Object Detection},
booktitle = CVPR,
year = {2019}
}
%PoolNet
@inproceedings{Liu2019PoolSal,
title={A Simple Pooling-Based Design for Real-Time Salient Object Detection},
author={Liu, Jiang-Jiang and Hou, Qibin and Cheng, Ming-Ming and Feng, Jiashi and Jiang, Jianmin},
booktitle = CVPR,
year = {2019},
}
%AFNet
@InProceedings{Feng_2019_CVPR,
author = {Feng, Mengyang and Lu, Huchuan and Ding, Errui},
title = {Attentive Feedback Network for Boundary-aware Salient Object Detection},
booktitle = CVPR,
year = {2019}
}
% BMPM
@inproceedings{zhang2018bi,
title={A Bi-Directional Message Passing Model for Salient Object Detection},
author={Zhang, Lu and Dai, Ju and Lu, Huchuan and He, You and Wang, Gang},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={1741--1750},
year={2018}
}
% DGRL
@inproceedings{wang2018detect,
title={Detect Globally, Refine Locally: A Novel Approach to Saliency Detection},
author={Wang, Tiantian and Zhang, Lihe and Wang, Shuo and Lu, Huchuan and Yang, Gang and Ruan, Xiang and Borji, Ali},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={3127--3135},
year={2018}
}
% PAGR
@inproceedings{zhang2018progressive,
title={Progressive Attention Guided Recurrent Network for Salient Object Detection},
author={Zhang, Xiaoning and Wang, Tiantian and Qi, Jinqing and Lu, Huchuan and Wang, Gang},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={714--722},
year={2018}
}
% RAS
@inproceedings{chen2018eccv,
author={Chen, Shuhan and Tan, Xiuli and Wang, Ben and Hu, Xuelong},
booktitle={European Conference on Computer Vision},
title={Reverse Attention for Salient Object Detection},
year={2018}
}
% PiCANet
@inproceedings{liu2018picanet,
title={PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection},
author={Liu, Nian and Han, Junwei and Yang, Ming-Hsuan},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={3089--3098},
year={2018}
}
% R3Net
@inproceedings{deng18r,
author = {Deng, Zijun and Hu, Xiaowei and Zhu, Lei and Xu, Xuemiao and Qin, Jing and Han, Guoqiang and Heng, Pheng-Ann},
title = {R$^{3}${N}et: Recurrent Residual Refinement Network for Saliency Detection},
booktitle = {IJCAI},
year = {2018}
}
% Amulet
@inproceedings{zhang2017amulet,
title={Amulet: Aggregating multi-level convolutional features for salient object detection},
author={Zhang, Pingping and Wang, Dong and Lu, Huchuan and Wang, Hongyu and Ruan, Xiang},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={202--211},
year={2017}
}
% UCF
@inproceedings{zhang2017learning,
title={Learning uncertain convolutional features for accurate saliency detection},
author={Zhang, Pingping and Wang, Dong and Lu, Huchuan and Wang, Hongyu and Yin, Baocai},
booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
pages={212--221},
year={2017},
organization={IEEE}
}
% SRM
@inproceedings{wang2017stagewise,
title={A stagewise refinement model for detecting salient objects in images},
author={Wang, Tiantian and Borji, Ali and Zhang, Lihe and Zhang, Pingping and Lu, Huchuan},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={4019--4028},
year={2017}
}
% MSRNet
@inproceedings{li2017instance,
title={Instance-level salient object segmentation},
author={Li, Guanbin and Xie, Yuan and Lin, Liang and Yu, Yizhou},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
pages={247--256},
year={2017},
organization={IEEE}
}
% NLDF
@inproceedings{luo2017non,
title={Non-local Deep Features for Salient Object Detection.},
author={Luo, Zhiming and Mishra, Akshaya Kumar and Achkar, Andrew and Eichel, Justin A and Li, Shaozi and Jodoin, Pierre-Marc},
booktitle={CVPR},
volume={2},
number={6},
pages={7},
year={2017}
}
% DSS
@inproceedings{hou2017deeply,
title={Deeply supervised salient object detection with short connections},
author={Hou, Qibin and Cheng, Ming-Ming and Hu, Xiaowei and Borji, Ali and Tu, Zhuowen and Torr, Philip},
booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={5300--5309},
year={2017},
organization={IEEE}
}
% RFCN
@inproceedings{wang2016saliency,
title={Saliency detection with recurrent fully convolutional networks},
author={Wang, Linzhao and Wang, Lijun and Lu, Huchuan and Zhang, Pingping and Ruan, Xiang},
booktitle={European Conference on Computer Vision},
pages={825--841},
year={2016},
organization={Springer}
}
% SCSD-HS
@inproceedings{kim2016shape,
title={A shape preserving approach for salient object detection using convolutional neural networks},
author={Kim, Jongpil and Pavlovic, Vladimir},
booktitle={Pattern Recognition (ICPR), 2016 23rd International Conference on},
pages={609--614},
year={2016},
organization={IEEE}
}
% DS
@article{li2016deepsaliency,
title={Deepsaliency: Multi-task deep neural network model for salient object detection},
author={Li, Xi and Zhao, Liming and Wei, Lina and Yang, Ming-Hsuan and Wu, Fei and Zhuang, Yueting and Ling, Haibin and Wang, Jingdong},
journal={IEEE Transactions on Image Processing},
volume={25},
number={8},
pages={3919--3930},
year={2016},
publisher={IEEE}
}
% ELD
@inproceedings{lee2016deep,
title={Deep saliency with encoded low level distance map and high level features},
author={Lee, Gayoung and Tai, Yu-Wing and Kim, Junmo},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={660--668},
year={2016}
}
% DCL
@inproceedings{li2016deep,
title={Deep contrast learning for salient object detection},
author={Li, Guanbin and Yu, Yizhou},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={478--487},
year={2016}
}
% DHSNet
@inproceedings{liu2016dhsnet,
title={Dhsnet: Deep hierarchical saliency network for salient object detection},
author={Liu, Nian and Han, Junwei},
booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={678--686},
year={2016},
organization={IEEE}
}
% LEGS
@inproceedings{wang2015deep,
title={Deep networks for saliency detection via local estimation and global search},
author={Wang, Lijun and Lu, Huchuan and Ruan, Xiang and Yang, Ming-Hsuan},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={3183--3192},
year={2015}
}
% MCDL
@inproceedings{zhao2015saliency,
title={Saliency detection by multi-context deep learning},
author={Zhao, Rui and Ouyang, Wanli and Li, Hongsheng and Wang, Xiaogang},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={1265--1274},
year={2015}
}
% MDF
@inproceedings{li2015visual,
title={Visual saliency based on multiscale deep features},
author={Li, Guanbin and Yu, Yizhou},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5455--5463},
year={2015}
}
- add scores in Table
- add pre-computed saliency maps
- add pre-computed
.mat
files - add evaluation codes
If you find this code useful in your research, please consider citing:
@article{sal_eval_toolbox,
Author = {Mengyang Feng},
Title = {Evaluation Toolbox for Salient Object Detection.},
Journal = {https://github.com/ArcherFMY/sal_eval_toolbox},
Year = {2018}
}