MIT Saliency Benchmark Results: MIT300

The following are results of models evaluated on their ability to predict ground truth human fixations on our benchmark data set containing 300 natural images with eye tracking data from 39 observers. We post the results here and provide a way for people to submit new models for evaluation.

citations

If you use any of the results or data on this page, please cite the following:

@misc{mit-saliency-benchmark,
  author     = {Zoya Bylinskii and Tilke Judd and Ali Borji and Laurent Itti and Fr{\'e}do Durand and Aude Oliva and Antonio Torralba},
  title      = {MIT Saliency Benchmark},
}

These evaluations are released in conjuction with the following papers:

@article{salMetrics_Bylinskii,
  title     = {What do different evaluation metrics tell us about saliency models?},
  author    = {Zoya Bylinskii and Tilke Judd and Aude Oliva and Antonio Torralba and Fr{\'e}do Durand},
  journal   = {arXiv preprint arXiv:1604.03605},
  year      = {2016}
}
@InProceedings{Judd_2012,
  title     = {A Benchmark of Computational Models of Saliency to Predict Human Fixations},
  author    = {Tilke Judd and Fr{\'e}do Durand and Antonio Torralba},
  booktitle = {MIT Technical Report},
  year      = {2012}
}

Images

300 benchmark images (the fixations from 39 viewers per image are not public such that no model can be trained using this data set).

Model Performances

Model Visualizations

68 models, 5 baselines, 8 metrics, and counting...

Performance numbers prior to September 25, 2014.

Matlab code for the metrics we use.

Sorted by: metric


NOTE: MIT Saliency Benchmark will soon switch to sorting model performances by NSS
This decision has been made at ECCV 2016 saliency tutorial. See:
Z Bylinskii, T Judd, A Oliva, A Torralba, F Durand What do different evaluation metrics tell us about saliency models? arXiv preprint arXiv:1604.03605, 2016
M K├╝mmerer, T Wallis, M Bethge Information-theoretic model comparison unifies saliency metrics PNAS, 112(52), 16054-16059, 2015

Model Name Published Code AUC-Judd [?] SIM [?] EMD [?] AUC-Borji [?] sAUC [?] CC [?] NSS [?] KL [?] Date tested [key] Sample [img]
Baseline: infinite humans [?] 0.92 1 0 0.88 0.81 1 3.29 0
Deep Gaze 1 Matthias Kümmerer, Lucas Theis, Matthias Bethge. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet [arxiv 2014, ICLR 2015 workshop] 0.84 0.39 4.97 0.83 0.66 0.48 1.22 1.23 first tested: 02/10/2014
last tested: 15/11/2015
maps from authors
Deep Gaze 2 Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge. DeepGaze II: Reading fixations from deep features trained on object recognition [arXiv 2016] 0.88
(0.84)
0.46
(0.43)
3.98
(4.52)
0.86
(0.83)
0.72
(0.77)
0.52
(0.45)
1.29
(1.16)
0.96
(1.04)
first tested: 26/11/2015
last tested: 13/09/2016
maps from authors
(model without center bias in parentheses)
Boolean Map based Saliency (BMS) Jianming Zhang, Stan Sclaroff. Saliency detection: a boolean map approach [ICCV 2013] matlab, executable 0.83 0.51 3.35 0.82 0.65 0.55 1.41 0.81 first tested: 14/05/2014
last tested: 23/09/2014
maps from authors
Mixture of Saliency Models Xuehua Han, Shunji Satoh. "Unifying computational models for visual attention" [AINI 2014, Sep. (accepted)] 0.82 0.44 4.22 0.81 0.62 0.52 1.34 0.91 first tested: 08/08/2014
last tested: 23/09/2014
maps from authors
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 0.82 0.41 4.56 0.81 0.62 0.45 1.14 1.14 first tested: 16/08/2014
last tested: 15/11/2015
maps from authors
Judd Model Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba. Learning to predict where humans look [ICCV 2009] matlab 0.81 0.42 4.45 0.80 0.60 0.47 1.18 1.12 last tested: 14/11/2015
maps from code (DL:17/12/2013) with default params
CovSal Erkut Erdem, Aykut Erdem. Visual saliency estimation by nonlinearly integrating features using region covariances [JoV 2013] matlab 0.81 0.47 3.39 0.67 0.57 0.45 1.22 2.68 first tested: 05/02/2012
last tested: 23/09/2014
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 0.80 0.49 3.36 0.73 0.59 0.48 1.27 1.20 first tested: 04/10/2013
last tested: 15/11/2015
maps from authors
Graph-Based Visual Saliency (GBVS) Jonathan Harel, Christof Koch, Pietro Perona. Graph-Based Visual Saliency [NIPS 2006] matlab 0.81 0.48 3.51 0.80 0.63 0.48 1.24 0.87 last tested: 23/09/2014
maps from code (DL:20/08/2013) with default params
Spatially Weighted Dissimilarity Saliency (SWD) Lijuan Duan, Chunpeng Wu, Jun Miao, Laiyun Qing, Yu Fu. Visual Saliency Detection by Spatially Weighted Dissimilarity [CVPR 2011] matlab 0.81 0.46 3.89 0.80 0.59 0.49 1.27 0.97 first tested: 22/09/2014
last tested: 15/11/2015
maps from authors
Baseline: one human [?] 0.80
min: 0.76
max: 0.83
0.38
min: 0.33
max: 0.46
3.48
min: 2.88
max: 4.18
0.66
min: 0.63
max: 0.71
0.63
min: 0.60
max: 0.67
0.52
min: 0.38
max: 0.65
1.65
min: 1.21
max: 2.10
6.19
min: 4.76
max: 8.37
Sampled Template Collation Andreas Holzbach, Gordon Cheng. A Scalable and Efficient Method for Salient Region Detection using Sampled Template Collation [ICIP 2014] 0.79 0.39 4.79 0.78 0.54 0.40 0.97 1.23 first tested: 04/12/2013
last tested: 14/11/2015
maps from authors
Region Contrast (RC) Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, Shi-Min Hu. Global Contrast based Salient Region detection [IEEE TPAMI 2014]
Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, Shi-Min Hu. Salient Object Detection and Segmentation [CVPR 2011]
c++, executable 0.79 0.48 3.48 0.78 0.55 0.47 1.18 0.93 first tested: 03/08/2013
last tested: 15/11/2015
maps from authors
Multi-Resolution AIM (MR-AIM) Siddharth Advani, John Sustersic, Kevin Irick, Vijaykrishnan Narayanan. A multi-resolution saliency framework to drive foveation [ICASSP 2013] C++ 0.75 0.43 4.10 0.75 0.59 0.36 0.90 2.60 first tested: 27/05/2013
last tested: 11/01/2016
maps from authors
CWS model [unpublished] 0.79 0.46 3.81 0.78 0.55 0.45 1.11 0.99 first tested: 14/05/2014
last tested: 23/09/2014
maps from authors
MKL-based model Yasin Kavak, Aykut Erdem, Erkut Erdem. Visual saliency estimation by integrating features using multiple kernel learning [ISACS 2013] 0.78 0.42 4.40 0.78 0.61 0.42 1.08 1.10 first tested: 17/03/2014
last tested: 15/11/2015
maps from authors
Baseline: Center [?] matlab 0.78 0.45 3.72 0.77 0.51 0.38 0.92 1.24
Saliency for Image Manipulation Ran Margolin, Lihi Zelnik-Manor, Ayellet Tal. Saliency for Image Manipulation [CGI 2012] matlab 0.77 0.46 4.17 0.76 0.64 0.43 1.14 1.53 first tested: 01/07/2012
last tested: 15/11/2015
maps from authors
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 0.77 0.46 4.11 0.75 0.67 0.42 1.15 1.01 first tested: 31/08/2012
last tested: 15/11/2015
maps from authors
LMF Liang Jiayu Gary. [unpublished] 0.77 0.45 4.22 0.76 0.64 0.41 1.07 1.02 first tested: 06/10/2013
last tested: 14/11/2015
maps from authors
AIM Neil Bruce, John Tsotsos. Attention based on information maximization [JoV 2007] matlab 0.77 0.40 4.73 0.75 0.66 0.31 0.79 1.18 last tested: 23/09/2014
maps from code (DL:15/01/2014) with params: resize=0.5, convolve=1, thebasis='31infomax975'
Random Center Surround Saliency Tadmeri Narayan Vikrama, Marko Tscherepanowa, Britta Wredea. A saliency map based on sampling an image into random rectangular regions of interest [Pattern Recognition 2012] matlab 0.75 0.44 3.81 0.74 0.55 0.38 0.95 1.08 first tested: 15/05/2012
last tested: 15/11/2015
maps from authors
Image Signature Xiaodi Hou, Jonathan Harel, Christof Koch. Image Signature: Highlighting Sparse Salient Regions [PAMI 2011] matlab 0.75 0.43 4.49 0.74 0.66 0.38 1.01 1.09 first tested: 19/06/2014
last tested: 15/11/2015
maps from authors
IttiKoch2 Implementation by Jonathan Harel (part of GBVS toolbox) matlab 0.75 0.44 4.26 0.74 0.63 0.37 0.97 1.03 last tested: 23/09/2014
maps from code (DL:20/08/2013) with default params
Visual Conspicuity (VICO) Matthieu Perreira Da Silva, Vincent Courboulay. Implementation and Evaluation of a Computational Model of Attention for Computer Vision [book chapter, 2012] binaries 0.75 0.44 4.38 0.71 0.60 0.37 0.97 1.96 first tested: 28/11/2012
last tested: 15/11/2015
maps from authors
Aboudib Magnification Saliency (Bottom-up v1) Ala Aboudib, Vincent Gripon, Gilles Coppin. A model of bottom-up visual attention using cortical magnification [ICASSP 2015] 0.74 0.44 4.24 0.72 0.58 0.39 0.99 2.45 first tested: 23/09/2014
last tested: 29/09/2014
maps from authors
Aboudib Magnification Saliency (Bottom-up v2) Ala Aboudib, Vincent Gripon, Gilles Coppin. [unpublished] python 0.78 0.48 3.56 0.75 0.56 0.45 1.12 1.19 first tested: 22/04/2015
last tested: 22/04/2015
maps from authors
Context-Aware saliency Stas Goferman, Lihi Zelnik-Manor, Ayellet Tal. Context-Aware Saliency Detection [CVPR 2010] [PAMI 2012] matlab 0.74 0.43 4.46 0.73 0.65 0.36 0.95 1.06 last tested: 23/09/2014
maps from code (DL:15/01/2014) with default params
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 0.74 0.43 4.62 0.73 0.68 0.37 1.01 1.07 last tested: 23/09/2014
maps from code (DL:17/01/2014) with params: rescale=0.5
Weighted Maximum Phase Alignment Model (WMAP) Fernando Lopez-Garcia, Xose Ramon Fdez-Vidal, Xose Manuel Pardo, Raquel Dosil. Scene Recognition through Visual Attention and Image Features: A Comparison between SIFT and SURF Approaches matlab 0.74 0.42 4.49 0.67 0.63 0.34 0.97 1.38 last tested: 23/09/2014
maps from code (DL:17/01/2014) with params: rescale=0.5
Generalized Nonlocal Mean Saliency (GNM) Guangyu Zhong, Risheng Liu, Junjie Cao and Zhixun Su. A generalized nonlocal mean framework with object-level cues for saliency detection [The Visual Computer 2015] 0.74 0.42 4.49 0.67 0.63 0.34 0.97 1.21 first tested: 02/03/2015
last tested: 02/03/2015
maps from authors
NARFI saliency Jiazhong Chen, Hua Cao, Zengwei Ju, Leihua Qin, Shuguang Su. Non-attention region first initialisation of k-means clustering for saliency detection [Electronics Letters 2013] 0.73 0.38 4.75 0.61 0.55 0.31 0.83 5.17 first tested: 11/05/2013
last tested: 14/11/2015
maps from authors
Self-resemblance by LARK Hae Jong Seo, Peyman Milanfar. Static and Space-time Visual Saliency Detection by Self-Resemblance [JoV 2012] matlab 0.71 0.41 4.55 0.69 0.64 0.31 0.83 1.54 first tested: 20/06/2014
last tested: 15/11/2015
maps from authors
Local Saliency Model (LS) Ali Borji, Laurent Itti. Exploiting local and global patch rarities for saliency detection. [CVPR 2012] matlab 0.78 0.43 4.40 0.77 0.64 0.39 1.02 1.16 first tested: 27/11/2014
last tested: 15/11/2015
maps from authors
Local+Global Saliency Model (LGS) Ali Borji, Laurent Itti. Exploiting local and global patch rarities for saliency detection. [CVPR 2012] matlab 0.76 0.42 4.63 0.76 0.66 0.39 1.02 1.11 first tested: 27/11/2014
last tested: 15/11/2015
maps from authors
Stochastic fixation prediction (SFP) Hamed Rezazadegan Tavakoli, Esa Rahtu, Janne Heikkila. Stochastic bottom-up fixation prediction and saccade generation. [Image and Vision Computing 2013] 0.71 0.41 4.56 0.70 0.62 0.30 0.80 1.20 first tested: 23/12/2014
last tested: 14/11/2015
maps from authors
Murray model (Chromatic Induction Wavelet Model) Naila Murray, Maria Vanrell, Xavier Otazu, C. Alejandro Parraga. Saliency Estimation Using a Non-Parametric Low-Level Vision Model [CVPR 2011] matlab 0.70 0.38 5.18 0.69 0.65 0.27 0.73 1.23 last tested: 23/09/2014
maps from code (DL:29/05/2014) with default params
Quantum-Cuts (QCUT) Caglar Aytekin, Serkan Kiranyaz, Moncef Gabbouj. Automatic Object Segmentation by Quantum Cuts [ICPR 2014] 0.75 0.39 4.57 0.67 0.57 0.40 1.07 6.71 first tested: 19/12/2013
last tested: 15/11/2015
maps from authors
Torralba saliency Antonio Torralba, Aude Oliva, Monica S. Castelhano, John M. Henderson. Contextual Guidance of Attention in Natural scenes: The role of Global features on object search [Psychological Review 2006] matlab 0.68 0.39 4.99 0.68 0.62 0.25 0.69 1.24 last tested: 23/09/2014
maps from code (here) with default params
Baseline: Permutation Control [?] 0.68 0.34 4.59 0.59 0.50 0.20 0.49 6.12
SUN saliency 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 0.67 0.38 5.10 0.66 0.61 0.25 0.68 1.27 last tested: 23/09/2014
maps from code (DL:15/01/2014) with params: scale=0.5
IttiKoch Implemented in the Saliency Toolbox by: Dirk Walther, Christof Koch. Modeling attention to salient proto-objects [Neural Networks 2006] matlab 0.60 0.20 5.17 0.54 0.53 0.14 0.43 2.30 last tested: 23/09/2014
maps from code (DL:15/01/2014) with params: sampleFactor='dyadic'
Achanta Radhakrishna Achanta, Sheila Hemami, Francisco Estrada, Sabine Susstrunk. Frequency-tuned Salient Region Detection [CVPR 2009] matlab, c++, executable 0.52 0.29 5.77 0.52 0.52 0.04 0.13 1.73 last tested: 23/09/2014
maps from code (DL:15/01/2014) with params: GausParam=[3,3]
Baseline: Chance [?] matlab 0.50 0.33 6.35 0.50 0.50 0.00 0.00 2.09
Co-Occurrence Histogram based Saliency Shijian Lu, Cheston Tan, Joo-Hwee Lim. Robust and Efficient Saliency Modeling from Image Co-Occurrence Histograms [IEEE TPAMI 2014] 0.80 0.50 3.49 0.76 0.62 0.49 1.27 1.36 first tested: 03/11/2015
last tested: 25/11/2015
maps from authors
SALICON Xun Huang, Chengyao Shen, Xavier Boix, Qi Zhao 0.87 0.60 2.62 0.85 0.74 0.74 2.12 0.54 first tested: 19/11/2014
last tested: 15/11/2015
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Rosin Saliency 1 Paul L. Rosin. A Simple Method for Detecting Salient Regions [Pattern Recognition 2009] C 0.71 0.40 4.86 0.70 0.62 0.29 0.76 1.13 first tested: 15/06/2015
last tested: 15/06/2015
maps from authors using pyramid of graylevel edges
Rosin Saliency 2 Paul L. Rosin. A Simple Method for Detecting Salient Regions [Pattern Recognition 2009] C 0.78 0.48 3.43 0.73 0.53 0.45 1.13 1.21 first tested: 15/06/2015
last tested: 15/11/2015
maps from authors extending published method to use pyramid of colour edges and also applying a central prior
SalNet Junting Pan, Kevin McGuinness, Elisa Sayrol, Noel O'Connor, and Xavier Giro-i-Nieto. Shallow and Deep Convolutional Networks for Saliency Prediction [arXiv 2016] python 0.83 0.52 3.31 0.82 0.69 0.58 1.51 0.81 first tested: 17/06/2015
last tested: 15/11/2015
maps from authors
Multiresolution CNN (Mr-CNN) Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu. Predicting Eye Fixations using Convolutional Neural Networks [CVPR 2015] 0.79 0.48 3.71 0.75 0.69 0.48 1.37 1.08 first tested: 08/11/2015
last tested: 15/11/2015
maps from authors
DeepFix Srinivas S S Kruthiventi, Kumar Ayush, R. Venkatesh Babu
DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations [arXiv 2015]
0.87 0.67 2.04 0.80 0.71 0.78 2.26 0.63 first tested: 02/10/2015
last tested: 02/10/2015
maps from authors
RARE2012 - Improved Pierre Marighetto, Nicolas Riche, Matei Mancas. LSUN SALICON Challenge (http://lsun.cs.princeton.edu/leaderboard/#saliencysalicon) Improved from: matlab 0.81 0.48 3.74 0.80 0.66 0.51 1.34 0.89 first tested: 23/10/2015
last tested: 23/10/2015
maps from authors
CNN-VLM Hiroharu Kato and Tatsuya Harada. Visual Language Modeling on CNN Image Representations [arXiv 2015] 0.79 0.43 4.55 0.79 0.71 0.44 1.18 1.06 first tested: 16/10/2015
last tested: 03/11/2015
maps from authors
Fabrice Urban Brice Follet, Christel Chamaret, Olivier Le Meur, Thierry Baccino: Medium Spatial Frequencies, a Strong Predictor of Salience. Cognitive Computation 3(1): 37-47 (2011) 0.70 0.40 5.03 0.70 0.64 0.29 0.78 1.23 first tested: 03/11/2015
last tested: 03/11/2015
maps from authors
Outlier Saliency (OS) Chen C, Tang H, Lyu Z, et al. Saliency modeling via outlier detection. Journal of Electronic Imaging, 2014, 23(5): 053023-053023. 0.82 0.51 3.35 0.81 0.64 0.54 1.41 0.84 first tested: 16/09/2014
last tested: 14/11/2015
maps from authors
Kalman based saliency model (KalSal) Sourya Roy, Pabitra Mitra. Visual saliency detection: a Kalman filter based approach 0.80 0.44 4.18 0.79 0.64 0.46 1.18 1.03 first tested: 26/01/2016
last tested: 26/01/2016
maps from authors
JuntingNet Junting Pan and Xavier Giro. End-to-end convolutional network for saliency prediction python 0.80 0.46 4.06 0.79 0.64 0.54 1.43 0.96 first tested: 06/11/2015
last tested: 06/11/2015
maps from authors
Probability Distribution Prediction (PDP) Saumya Jetley, Naila Murray, Eleonora Vig. End-to-End Saliency Mapping via Probability Distribution Prediction [CVPR 2016] 0.85 0.60 2.58 0.80 0.73 0.70 2.05 0.92 first tested: 05/11/2015
last tested: 05/11/2015
maps from authors
CVPR 2016 submission #1843 (shallow convnet) Anonymous. 0.80 0.45 3.99 0.79 0.63 0.56 1.47 0.95 first tested: 26/11/2015
last tested: 26/11/2015
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SOS CNN Map Naoaki Yamashita, Tomohiro Tanaka [unpublished] 0.65 0.37 4.96 0.65 0.61 0.24 0.67 1.32 first tested: 08/03/2016
last tested: 08/03/2016
maps from authors
Weakly Pre-Learnt Saliency Model (WEPSAM) A.Lahiri, S.Roy, A.Santara, P.Mitra, P.K.Biswas. WEPSAM: Weakly Pre-Learnt Saliency Model [arxiv 2016] 0.80 0.45 4.22 0.78 0.62 0.51 1.36 1.00 first tested: 17/01/2016
last tested: 25/01/2016
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ML-Net Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara. A Deep Multi-Level Network for Saliency Prediction [ICPR 2016] Python 0.85 0.59 2.63 0.75 0.70 0.67 2.05 1.10 first tested: 25/01/2016
last tested: 01/09/2016
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Corner-based Saliency (CORS) Wirawit Rueopas. A corner-based saliency model [JCSSE 2016] Python 0.79 0.47 3.91 0.77 0.66 0.46 1.22 1.03 first tested: 30/03/2016
last tested: 30/03/2016
maps from authors
Salient Point Parzen Map (SPPM) Saulo Oliveira 0.77 0.46 4.17 0.76 0.66 0.42 1.10 1.18 first tested: 23/10/2016
last tested: 23/10/2016
maps from authors
Deep Spatial Contextual Long-term Recurrent Convolutional Network (DSCLRCN) Nian Liu, Junwei Han. A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection [arXiv 2016] 0.87 0.68 2.17 0.79 0.72 0.80 2.35 0.95 first tested: 16/06/2016
last tested: 27/07/2016
maps from authors
GMR C. Yang, L. Zhang, H. Lu, X. Ruan, M-H. Yang. Saliency detection via graph-based manifold ranking. [CVPR 2013] 0.74 0.38 4.28 0.64 0.53 0.36 0.94 7.38 first tested: 16/06/2016
last tested: 16/06/2016
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LDS Shu Fang, Jia Li, Yonghong Tian, Tiejun Huang, Xiaowu Chen. Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis [TNNLS 2016] Matlab 0.81 0.52 3.06 0.76 0.60 0.52 1.36 1.05 first tested: 13/09/2016
last tested: 13/09/2016
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iSEEL Hamed R.-Tavakoli et al. Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features [arXiv 2016] Matlab 0.84 0.57 2.72 0.81 0.68 0.65 1.78 0.65 first tested: 13/09/2016
last tested: 13/09/2016
maps from authors
I-VGG16 (UID) H. R.-Tavakoli and J. Laaksonen. "Bottom-up Fixation Prediction Using Unsupervised Hierarchical Models" [ACCV 2016 Workshop on Assistive Vision] Matlab 0.78 0.47 3.96 0.76 0.67 0.45 1.24 1.02 first tested: 10/06/2016
last tested: 10/06/2016
maps from authors
UHF H. R.-Tavakoli and J. Laaksonen. "Bottom-up Fixation Prediction Using Unsupervised Hierarchical Models" [ACCV 2016 Workshop on Assistive Vision] Matlab 0.80 0.45 4.11 0.79 0.64 0.47 1.21 1.00 first tested: 10/06/2016
last tested: 10/06/2016
maps from authors
SalGAN [unpublished] 0.86 0.63 2.29 0.81 0.72 0.73 2.04 1.07 first tested: 10/30/2016
last tested: 10/30/2016
maps from authors
Saliency Attentive Model (SAM) Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara. Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model [arXiv 2016] 0.87 0.67 2.06 0.79 0.71 0.79 2.31 1.19 first tested: 10/30/2016
last tested: 11/03/2016
maps from authors