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Full-Text Articles in Physical Sciences and Mathematics
Smiler: Consistent And Usable Saliency Model Implementations, Toni Kunic, Calden Wloka, John K. Tsotsos
Smiler: Consistent And Usable Saliency Model Implementations, Toni Kunic, Calden Wloka, John K. Tsotsos
MODVIS Workshop
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models. This work drastically reduces the human effort required to apply saliency algorithms to new tasks and datasets, while also ensuring consistency and procedural correctness for results and conclusions produced by different parties. At its launch SMILER already includes twenty three saliency models (fourteen models based in MATLAB and nine supported through containerization), and the open design of SMILER encourages this number to grow with future contributions from the community. The project …
Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
MODVIS Workshop
No abstract provided.
Consistent Saliency Benchmarking: How One Model Can Win On All Metrics, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
Consistent Saliency Benchmarking: How One Model Can Win On All Metrics, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
MODVIS Workshop
No abstract provided.
Focusing On Selection For Fixation, John K. Tsotsos, Calden Wloka, Yulia Kotseruba
Focusing On Selection For Fixation, John K. Tsotsos, Calden Wloka, Yulia Kotseruba
MODVIS Workshop
Building on our presentation at MODVIS 2015, we continue in our quest to discover a functional, computational, explanation of the relationship among visual attention, interpretation of visual stimuli, and eye movements, and how these produce visual behavior. Here, we focus on one component, how selection is accomplished for the next fixation. The popularity of saliency map models drives the inference that this is solved; we suggested otherwise at MODVIS 2015. Here, we provide additional empirical and theoretical arguments. We then develop arguments that a cluster of complementary, conspicuity representations drive selection, modulated by task goals and history, leading to a …