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20172026
most citedGuiding human gaze with convolutional neural networks

13 citations · 14 across the 3 of their papers we have counts for

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cs.CV2026

Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking

Susmit Agrawal, Jannis Hollman, Matthias Kümmerer

Empirical fixation densities, spatial distributions estimated from human eye-tracking data, are foundational to saliency benchmarking. They directly shape benchmark conclusions, le…

cs.CV2025

Modeling Saliency Dataset Bias

Matthias Kümmerer, Harneet Singh Khanuja, Matthias Bethge

Recent advances in image-based saliency prediction are approaching gold standard performance levels on existing benchmarks. Despite this success, we show that predicting fixations…

cs.CV20241 cited

Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli

Matthias Tangemann, Matthias Kümmerer, Matthias Bethge

Humans excel at detecting and segmenting moving objects according to the Gestalt principle of "common fate". Remarkably, previous works have shown that human perception generalizes…

cs.CV2021

State-of-the-Art in Human Scanpath Prediction

Matthias Kümmerer, Matthias Bethge

The last years have seen a surge in models predicting the scanpaths of fixations made by humans when viewing images. However, the field is lacking a principled comparison of those…

cs.CV201713 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…