显著性目标检测

1 每个数据库单独训练

方法	ECSSD		PASCAL-S		DUTS-test		HKU-IS		SOD		DUT-OMRON		时间	期刊	备注
		MAE		MAE		MAE		MAE		MAE		MAE
SRNet-V[1]	0.938	0.045	0.868	0.078	0.869	0.047	0.929	0.038	0.851	0.084	0.802	0.067	2019	arxiv	以VGG为骨架
SRNet-R [1]	0.944	0.04	0.883	0.075	0.878	0.045	0.930	0.036	0.859	0.076	0.830	0.060			以ResNet为骨架

2 用MSRA10K或者DUTS -TR作为训练集，其他数据库作为测试集

方法	ECSSD		PASCAL-S		DUTS-test		HKU-IS		SOD		DUT-OMRON		时间	期刊	备注
		MAE		MAE		MAE		MAE		MAE		MAE
SFCN[2]	0.911	0.0421	0.813	0.0732	0.742	0.0622	0.906	0.0357	0.822	0.1012	0.718	0.0643	2019	arxiv
RDS-1152[3]	0.953	0.036	0.874	0.08	0.867	0.044	0.942	0.028	0.817	0.083	0.837	0.05	2019	arxiv	使用目标检测的数据集进行辅助
DGRL[4]	0.903	0.045	-	-	0.768	0.051	0.882	0.037	-	-	0.709	0.063	2018	cvpr2018
PAGRN[5]	0.891	0.064	0.803	0.092	0.788	0.055	0.886	0.048	-	-	0.711	0.072	2018	cvpr2018	353X353的输出
PiCANet[6]	0.931	0.047	0.88	0.0781	0.851	0.054	0.8921	0.042	0.855	0.108	0.794	0.068	2018	cvpr2018

多模态显著性目标检测

RGB-D显著性目标检测

模型	NJUD		NLRP		STEREO		DES		发表时间	期刊	备注
		MAE		MAE		MAE		MAE
AF[7]	0.899	0.0534	0.899	0.0327	0.904	0.0462	--	--	2019	arxiv
MV-CNN[8]	---	---	---	---	---	---	---	---	2018	IEEE

参考文献

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1. Deep Reasoning with Multi-scale Context for Salient Object Detection,Zun Li,2019,http://arxiv.org/abs/1901.08362 ↩ ↩

2. Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss , Pingping Zhang, https://arxiv.org/abs/1901.06823 ↩

3. Richer and Deeper Supervision Network for Salient Object Detection, Sen Jia，Neil D. B. Bruce,https://www.jianshu.com/go-wild?ac=2&url=https://arxiv.org/abs/1901.02425 ↩

4. Detect Globally, Refine Locally: A Novel Approach to Saliency Detection, Tiantian Wang,链接：https://www.crcv.ucf.edu/papers/cvpr2018/camera_ready.pdf， 代码：https://github.com/TiantianWang/CVPR18_detect_globally_refine_locally ↩

5. Progressive Attention Guided Recurrent Network for Salient Object Detection,Xiaoning Zhang,链接：http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Progressive_Attention_Guided_CVPR_2018_paper.html， 代码：https://github.com/zhangxiaoning666/PAGR ↩

6. PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection,Nian Liu,链接：http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_PiCANet_Learning_Pixel-Wise_CVPR_2018_paper.pdf
   ， 代码：https://github.com/nian-liu/PiCANet ↩

7. Adaptive Fusion for RGB-D Salient Object Detection Ningning, Ningning Wang, Xiaojin Gong， 2019， https://arxiv.org/abs/1901.01369 ↩

8. CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion,Junwei Han,2018,https://ieeexplore.ieee.org/document/8091125 ↩