activity
20142021
most citedDeepGaze II: Reading fixations from deep features trained on object recognition

262 citations · 370 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2021★ 19 cited

How Well do Feature Visualizations Support Causal Understanding of CNN Activations?

Roland S. Zimmermann, Judy Borowski, Robert Geirhos +3

A precise understanding of why units in an artificial network respond to certain stimuli would constitute a big step towards explainable artificial intelligence. One widely used ap…

cs.CV2020★ 5 cited

Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization

Judy Borowski, Roland S. Zimmermann, Judith Schepers +4

Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural ne…

cs.CV2020★ 69 cited

Five Points to Check when Comparing Visual Perception in Humans and Machines

Christina M. Funke, Judy Borowski, Karolina Stosio +3

With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed towards comparing information processing in humans and machi…

cs.CV2017★ 13 cited

Guiding human gaze with convolutional neural networks

Leon A. Gatys, Matthias Kümmerer, Thomas S. A. Wallis +1

The eye fixation patterns of human observers are a fundamental indicator of the aspects of an image to which humans attend. Thus, manipulating fixation patterns to guide human atte…

cs.CV2017★ 1 cited

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to jud…

cs.CV2016★ 262 cited

DeepGaze II: Reading fixations from deep features trained on object recognition

Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge

Here we present DeepGaze II, a model that predicts where people look in images. The model uses the features from the VGG-19 deep neural network trained to identify objects in image…

cs.CV2014★ 1 cited

How close are we to understanding image-based saliency?

Matthias Kümmerer, Thomas Wallis, Matthias Bethge

Within the set of the many complex factors driving gaze placement, the properities of an image that are associated with fixations under free viewing conditions have been studied ex…