Methods for comparing scanpaths and saliency maps: strengths and weaknesses.
Olivier Le Meur,Thierry Baccino +1 more
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The strengths and weaknesses of common assessment methods based on diachronic eye-tracking data are surveyed and the use of some methods to benchmark computational models of visual attention is illustrated.Abstract:
In this article, we are interested in the computational modeling of visual attention. We report methods commonly used to assess the performance of these kinds of models. We survey the strengths and weaknesses of common assessment methods based on diachronic eye-tracking data. We then illustrate the use of some methods to benchmark computational models of visual attention.read more
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Journal ArticleDOI
What Do Different Evaluation Metrics Tell Us About Saliency Models
TL;DR: This paper provides an analysis of 8 different evaluation metrics and their properties, and makes recommendations for metric selections under specific assumptions and for specific applications.
Journal ArticleDOI
DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations
TL;DR: DeepFix as mentioned in this paper proposes a fully convolutional neural network (FCN) which models the bottom-up mechanism of visual attention via saliency prediction and predicts the saliency map in an end-to-end manner.
Proceedings ArticleDOI
Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics
TL;DR: This paper compares the ranking of 12 state-of-the art saliency models using 12 similarity metrics and shows that some of the metrics are strongly correlated leading to a redundancy in the performance metrics reported in the available benchmarks.
Proceedings ArticleDOI
Analysis of Scores, Datasets, and Models in Visual Saliency Prediction
TL;DR: A critical and quantitative look at challenges in saliency modeling and the way they affect model accuracy is pursued, providing a comprehensive high-level picture of the strengths and weaknesses of many popular models, and suggests future research directions in Saliency modeling.
Posted Content
Saliency in VR: How do people explore virtual environments?
TL;DR: This work captures and analyzes gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions, which leads to several important insights, such as the existence of a particular fixation bias.
References
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Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
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Signal detection theory and psychophysics
David M. Green,John A. Swets +1 more
TL;DR: This book discusses statistical decision theory and sensory processes in signal detection theory and psychophysics and describes how these processes affect decision-making.
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A model of saliency-based visual attention for rapid scene analysis
TL;DR: In this article, a visual attention system inspired by the behavior and the neuronal architecture of the early primate visual system is presented, where multiscale image features are combined into a single topographical saliency map.