MIT/Tuebingen Saliency Benchmark

Leaderboard MIT300

Click on a metric name to sort that metric. Models evaluated as probabilistic models are shown with green background. The performance under the metric which a model has been trained on is shown in italic. The code for evaluation models can be found here.

Name	Published	Code	IG	AUC	sAUC	NSS	CC	KLDiv	SIM	Date tested
IttiKoch	Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259	matlab		0.5434	0.5357	0.4081	0.1307	1.4964	0.3378	First tested 2019-09-14 Last tested 2019-09-14 maps from SaliencyToolBox via pysaliency
http://cseweb.ucsd.edu/~l6zhang/	Lingyun Zhang, Matthew H. Tong, Tim K. Marks, Honghao Shan, Garrison W. Cottrell. SUN: A Bayesian framework for saliency using natural statistics [JoV 2008]	matlab		0.6939	0.6260	0.7620	0.2770	1.2815	0.3931	First tested 2019-10-23 Last tested 2019-10-23 maps from code via SMILER. Params: rescale=0.5
Saliency Detection by Self-Resemblance (SSR)	Hae Jong Seo, Peyman Milanfar Nonparametric Bottom-Up Saliency Detection by Self-Resemblance [CVPR 2009].			0.7064	0.6482	0.8110	0.2999	1.5255	0.4124	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
Quaternion-Based Spectral Saliency (QSS)	B. Schauerte, R. Stiefelhagen. Quaternion-based Spectral Saliency Detection for Eye Fixation Prediction [ECCV 2012]	matlab		0.7233	0.6679	0.9116	0.3300	1.1431	0.4208	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
Image Signature	Xiaodi Hou, Jonathan Harel, Christof Koch. Image Signature: Highlighting Sparse Salient Regions [PAMI 2011]	matlab		0.7461	0.6610	0.9907	0.3709	1.0897	0.4278	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
Dynamic Visual Attention (DVA)	Hou, Xiaodi, and Liqing Zhang. Dynamic visual attention: Searching for coding length increments NIPS 2008	matlab		0.7548	0.6584	1.0142	0.3762	1.1136	0.4518	First tested 2019-09-04 Last tested 2019-09-04 maps from SMILER
Context-Aware Saliency	Stas Goferman, Lihi Zelnik-Manor, Ayellet Tal. Context-Aware Saliency Detection [CVPR 2010] [PAMI 2012]	matlab		0.7581	0.6402	1.0186	0.3848	1.0723	0.4319	First tested 2019-09-04 Last tested 2019-09-04 maps from SMILER
Adaptive Whitening Saliency Model (AWS)	Anton Garcia-Diaz, Victor Leboran, Xose R. Fdez-Vidal, Xose M. Pardo. On the relationship between optical variability, visual saliency, and eye fixations: A computational approach [JoV 2012]	matlab		0.7613	0.6761	1.0860	0.4009	1.0435	0.4397	First tested 2022-10-25 Last tested 2022-10-25 maps from code via SMILER. Params: resize=0.5
AIM	Neil Bruce, John Tsotsos. Attention based on information maximization [JoV 2007]	matlab		0.7619	0.6647	0.8824	0.3419	1.2476	0.4096	First tested 2019-09-10 Last tested 2019-09-10 maps from code via SMILER. Params: resize=0.5, thebasis='31infomax975'
RARE2012	Nicolas Riche, Matei Mancas, Matthieu Duvinage, Makiese Mibulumukini, Bernard Gosselin, Thierry Dutoit. RARE2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis [Signal Processing: Image Communication, 2013]	matlab		0.7700	0.6729	1.1513	0.4220	1.0090	0.4572	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
Boolean Map based Saliency (BMS)	Jianming Zhang, Stan Sclaroff. Saliency detection: a boolean map approach [ICCV 2013]	matlab, executable		0.7718	0.6918	1.1512	0.4130	1.0235	0.4456	First tested 2019-09-04 Last tested 2019-09-04 maps from SMILER
IttiKoch2	Implementation by Jonathan Harel (part of GBVS toolbox)	matlab		0.7811	0.6323	1.1130	0.4299	0.9605	0.4648	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
Centerbias	leave-one-image out kernel density estimate with uniform mixture component		0.0000	0.7830	0.1303	1.0960	0.4455	0.9506	0.4815	First tested 2019-09-10 Last tested 2021-11-24
CASPER V1 Salience	Rachel Heaton, Simona Buetti, Alejandro Lleras, and John Hummel			0.7941	0.6014	1.2093	0.4676	1.0295	0.4946	First tested 2021-09-07 Last tested 2021-09-07 maps from authors
Fast and Efficient Saliency (FES)	Hamed Rezazadegan Tavakoli, Esa Rahtu, Janne Heikkila. Fast and efficient saliency detection using sparse sampling and kernel density estimation [SCIA 2011]	matlab		0.8018	0.5941	1.2763	0.4827	2.3018	0.4919	First tested 2019-09-04 Last tested 2019-09-04 maps from SMILER
Graph-Based Visual Saliency (GBVS)	Jonathan Harel, Christof Koch, Pietro Perona. Graph-Based Visual Saliency [NIPS 2006]	matlab		0.8062	0.6299	1.2457	0.4791	0.8878	0.4835	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
Judd Model	Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba. Learning to predict where humans look [ICCV 2009]	matlab		0.8095	0.6003	1.1882	0.4664	1.1084	0.4182	First tested 2019-09-12 Last tested 2019-09-12 maps from SaliencyToolBox via pysaliency
LDS	Shu Fang, Jia Li, Yonghong Tian, Tiejun Huang, Xiaowu Chen. Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis [TNNLS 2016]	matlab		0.8108	0.6020	1.3649	0.5177	1.0631	0.5222	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
CovSal	Erkut Erdem, Aykut Erdem. Visual saliency estimation by nonlinearly integrating features using region covariances [JoV 2013]	matlab		0.8116	0.5894	1.3362	0.5000	1.7220	0.5058	First tested 2019-09-04 Last tested 2019-09-04 maps from SMILER
OpenSALICON	Christopher Lee Thomas. OpenSalicon: An Open Source Implementation of the Salicon Saliency Model [arXiv 2016]	python (caffe)		0.8140	0.7395	1.7029	0.5620	0.7829	0.5166	First tested 2019-09-06 Last tested 2019-09-06 maps from SMILER
Ensembles of Deep Networks (eDN)	Eleonora Vig, Michael Dorr, David Cox. Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images [CVPR 2014]	python		0.8171	0.6180	1.1399	0.4518	1.1369	0.4112	First tested 2019-09-06 Last tested 2019-09-06 maps from SMILER
ICF	Matthias Kümmerer, Thomas S. A. Wallis, Leon A. Gatys, Matthias Bethge. Understanding Low- and High-Level Contributions to Fixation Prediction [ICCV 2017]	python (tensorflow)	0.4140	0.8330	0.6957	1.6134	0.5876	0.7084	0.5576	First tested 2019-03-26 Last tested 2021-11-25
ML-Net	Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara. A Deep Multi-Level Network for Saliency Prediction [ICPR 2016]	python		0.8386	0.7399	1.9748	0.6633	0.8006	0.5819	First tested 2019-09-05 Last tested 2019-09-05 maps from SMILER
DeepGaze I	Matthias Kümmerer, Lucas Theis, Matthias Bethge. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet [arxiv 2014, ICLR 2015 workshop]		0.4836	0.8427	0.7232	1.7234	0.6144	0.6678	0.5717	First tested 2019-09-05 Last tested 2021-11-19 predictions from authors
Deep Visual Attention (DVA)	W. Wang, and J. Shen. Deep Visual Attention Prediction [IEEE TIP 2018]	matlab (caffe)		0.8430	0.7257	1.9305	0.6631	0.6293	0.5848	First tested 2019-09-04 Last tested 2019-09-04 maps from SMILER
Saliency Attentive Model (SAM-VGG)	Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara. Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model [IEEE TIP 2018]	python (theano)		0.8473	0.7305	1.9552	0.6630	1.2746	0.5986	First tested 2019-09-09 Last tested 2019-09-09 maps from SMILER
SalGAN	Junting Pan, Cristian Canton, Kevin McGuinness, Noel E. O'Connor, Jordi Torres, Elisa Sayrol and Xavier Giro-i-Nieto. SalGAN: Visual Saliency Prediction with Generative Adversarial Networks [arXiv 2017]	python		0.8498	0.7354	1.8620	0.6740	0.7574	0.5932	First tested 2019-09-06 Last tested 2019-09-06 maps from SMILER
Saliency Attentive Model (SAM-ResNet)	Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara. Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model [IEEE TIP 2018]	python (theano)		0.8526	0.7396	2.0628	0.6897	1.1710	0.6122	First tested 2019-09-09 Last tested 2019-09-09 maps from SMILER
CASNet II (Context Adaptive Saliency Network)	S. Fan, Z. Shen, M. Jiang, B. Koenig, J. Xu, M. Kankanhali, Q. Zhao. Emotional Attention: A Study of Image Sentiment and Visual Attention [CVPR 2018]	python (tensorflow)		0.8552	0.7398	1.9859	0.7054	0.5857	0.5806	First tested 2019-11-11 Last tested 2019-11-11 maps from authors
GazeGAN	Z. Che, A. Borji, G. Zhai, X. Min, G. Guo, P. Le Callet: How is Gaze Influenced by Image Transformations? Dataset and Model. IEEE Transactions on Image Processing (2019)	python (pytorch)		0.8607	0.7316	2.2118	0.7579	1.3390	0.6491	First tested 2020-04-21 Last tested 2020-04-21 maps from authors
AttentionInsight		demo		0.8640	0.7446	2.1825	0.7578	0.8873	0.6551	First tested 2019-06-29 Last tested 2021-11-22 maps from authors
TranSalNet	Jianxun Lou, Hanhe Lin, David Marshall, Dietmar Saupe and Hantao Liu: TranSalNet: Towards perceptually relevant visual saliency prediction. Neurocomputing 2022	python (pytorch)		0.8730	0.7471	2.3758	0.7991	0.9019	0.6852	First tested 2021-05-04 Last tested 2021-05-04 maps from authors
DeepGaze II	Matthias Kümmerer, Thomas S. A. Wallis, Leon A. Gatys, Matthias Bethge. Understanding Low- and High-Level Contributions to Fixation Prediction [ICCV 2017]	python (tensorflow)	0.9247	0.8733	0.7759	2.3371	0.7703	0.4239	0.6636	First tested 2019-09-11 Last tested 2021-11-16
TranSalNet_Dense	Jianxun Lou, Hanhe Lin, David Marshall, Dietmar Saupe and Hantao Liu: TranSalNet: Towards perceptually relevant visual saliency prediction. Neurocomputing 2022	python (pytorch)		0.8734	0.7467	2.4134	0.8070	1.0141	0.6895	First tested 2021-12-08 Last tested 2021-12-08 maps from authors
MSI-Net	A. Kroner, M. Senden, K. Driessens, R. Goebel: Contextual encoder–decoder network for visual saliency prediction. Neural Networks 2020	python (tensorflow)	0.9185	0.8738	0.7787	2.3053	0.7790	0.4232	0.6704	First tested 2020-05-14 Last tested 2021-11-15 maps from authors
HATES	paper in preparation			0.8744	0.7549	2.3762	0.7897	0.7146	0.5313	First tested 2021-11-24 Last tested 2021-11-24 maps from authors
Ani	(work in progress)			0.8748	0.7490	2.3518	0.7997	0.6741	0.6879	First tested 2022-05-21 Last tested 2022-05-21 maps from authors
EML-NET	Sen Jia & Neil D.B. Bruce EML-NET: An Expandable Multi-Layer NETwork for Saliency Prediction [arXiv 2018]	python (pytorch)		0.8762	0.7469	2.4876	0.7893	0.8439	0.6756	First tested 2019-07-06 Last tested 2019-07-06 maps from authors
SalFBNet	G. Ding, N. Imamoglu, A. Caglayan, M. Murakawa, R. Nakamura: SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks. Image and Vision Computing 2022.	python (pytorch)	0.8194	0.8769	0.7858	2.4702	0.8141	0.4151	0.6933	First tested 2021-11-08 Last tested 2021-11-08 maps from authors
UNISAL	R. Droste, J. Jiao, J.A. Noble: Unified Image and Video Saliency Modeling. ECCV 2020 (arXiv)	python (pytorch)	0.9505	0.8772	0.7840	2.3689	0.7851	0.4149	0.6746	First tested 2019-11-07 Last tested 2021-11-16 maps from authors, for probabilistic predictions see appendix of arxiv paper.
Clueify		Demo		0.8811	0.7651	1.4946	0.5750	0.8885	0.4773	First tested 2023-07-28 Last tested 2023-07-28 maps from authors
DeepGaze IIE	A. Linardos, M. Kümmerer, O. Press, M. Bethge: DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling [ICCV 2021]	python (pytorch)	1.0715	0.8829	0.7942	2.5265	0.8242	0.3474	0.6993	First tested 2020-09-24 Last tested 2020-09-24 maps from authors
Gold Standard (leave-one-subject-out)	Gaussian kernel density estimate using all fixations of an image with uniform mixture component. Crossvalidated over subjects. Leave-one-subject-out performance		1.3239	0.8982		2.8481				First tested 2019-10-24 Last tested 2022-11-10
Gold Standard	Gaussian kernel density estimate using all fixations of an image with uniform mixture component. Crossvalidated over subjects. Joint performance.		1.7366	0.9341	0.8825	3.1408	0.9828	0.0602	0.8992	First tested 2019-09-15 Last tested 2021-11-26

Baseline models

Gold Standard: Our gold standard model is a Gaussian Kernel Density Estimate. There are two versions of it. The crossvalidated performance is the leave-one-subject-out performance where for each subject and image the fixations of all other subject on the same image are used to construct a kernel density estimate that is then evaluated on the remaining subject. The kernel size and the mixture weight of a uniform regularization component are fitted by maximizing the cross-validated log-likelihood of the model. In addition to this crossvalidated version of the model, we also report the performance of a KDE model that uses all fixations on each image with the same parameters as the cross-validated model. One can interpret the cross-validated performance as a lower bound on the explainable performance and the joint performance as an upper bound.
Centerbias: The center bias model is again a Gaussian Kernel Density Estimate. However, unlike the gold standard, it uses the fixations of all other images to predict the fixations on any given image. Kernel size and the mixture weight oa a uniform regularization component are again fitted by maximizing the model log-likelihood.