Object-context inconsistencies affect gaze behavior differently than predicted by contextualized meaning maps
Abstract
ConclusionHuman observers look more at objects that are semantically inconsistent with the scene context compared to consistent objects. Contextualized meaning maps do not show a similar sensitivity to this manipulation. In fact, on average, raw rating data–from which these maps are generated–indicate that humans rate inconsistent objects as slightly less meaningful.
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.