Analyzing task-specific patterns in human scanpaths
Abstract
Humans gather high-resolution visual information only in the fovea, therefore they must make eye movements to explore the visual world. The spatio-temporal fixation patterns (scanpaths) of observers carry information about which aspects of the environment are currently relevant. Most of the recent progress on predicting the spatial and spatio-temporal patterns of human scanpaths has been focused on free-viewing conditions. However, fixations and scanpaths are known to be strongly influenced by the task performed by observers. The purpose of this work is to analyze those influences in a quantitative way. The DeepGaze III model for scanpath prediction (Kümmerer et al, VSS 2017) has been shown to achieve high performance in predicting free-viewing scanpaths. DeepGaze III extracts features from the VGG deep neural network that are used in a readout network to predict a saliency map, which is then …
Matthias Kümmerer
Postdoc
I’m interested in understanding how we use eye movements to gather information about our environment. This includes building saliency models and models of eye movement prediction such as my line of DeepGaze models. I also work on the question of how to evaluate model quality and benchmarking and I’m the main organizer of the MIT/Tuebingen Saliency Benchmark.
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center
Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.