27 citations · 110 across the 41 of their papers we have counts for
8 papers · 1 filter
Understanding Cross-Model Perceptual Invariances Through Ensemble Metamers
Lukas Boehm, Jonas Leo Mueller, Christoffer Loeffler +3
Understanding the perceptual invariances of artificial neural networks is essential for improving explainability and aligning models with human vision. Metamers - stimuli that are…
Joint Out-of-Distribution Filtering and Data Discovery Active Learning
Sebastian Schmidt, Leonard Schenk, Leo Schwinn +1
As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and redu…
Caption-Driven Explorations: Aligning Image and Text Embeddings through Human-Inspired Foveated Vision
Dario Zanca, Andrea Zugarini, Simon Dietz +4
Understanding human attention is crucial for vision science and AI. While many models exist for free-viewing, less is known about task-driven image exploration. To address this, we…
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Sebastian Schmidt, Leonard Schenk, Leo Schwinn +1
When applying deep learning models in open-world scenarios, active learning (AL) strategies are crucial for identifying label candidates from a nearly infinite amount of unlabeled…
Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors
Dario Zanca, Andrea Zugarini, Simon Dietz +4
Understanding the mechanisms underlying human attention is a fundamental challenge for both vision science and artificial intelligence. While numerous computational models of free-…
Simulating Human Gaze with Neural Visual Attention
Leo Schwinn, Doina Precup, Bjoern Eskofier +1
Existing models of human visual attention are generally unable to incorporate direct task guidance and therefore cannot model an intent or goal when exploring a scene. To integrate…
Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
Thomas Altstidl, An Nguyen, Leo Schwinn +4
The widespread success of convolutional neural networks may largely be attributed to their intrinsic property of translation equivariance. However, convolutions are not equivariant…
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
Leo Schwinn, Doina Precup, Björn Eskofier +1
By and large, existing computational models of visual attention tacitly assume perfect vision and full access to the stimulus and thereby deviate from foveated biological vision. M…