27 citations · 86 across the 23 of their papers we have counts for
26 papers
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-…
From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations
Toni Albert, Bjoern Eskofier, Dario Zanca
As the field of deep learning steadily transitions from the realm of academic research to practical application, the significance of self-supervised pretraining methods has become…
FastAMI -- a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
Kai Klede, Leo Schwinn, Dario Zanca +1
Clustering is at the very core of machine learning, and its applications proliferate with the increasing availability of data. However, as datasets grow, comparing clusterings with…
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…
Active Learning of Ordinal Embeddings: A User Study on Football Data
Christoffer Loeffler, Kion Fallah, Stefano Fenu +4
Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in informat…
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
Sami Ede, Serop Baghdadlian, Leander Weber +4
The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, th…
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Leo Schwinn, Leon Bungert, An Nguyen +5
The reliability of neural networks is essential for their use in safety-critical applications. Existing approaches generally aim at improving the robustness of neural networks to e…
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…
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series
Christoffer Loeffler, Wei-Cheng Lai, Bjoern Eskofier +3
The correct interpretation of convolutional models is a hard problem for time series data. While saliency methods promise visual validation of predictions for image and language pr…
SVC-onGoing: Signature Verification Competition
Ruben Tolosana, Ruben Vera-Rodriguez, Carlos Gonzalez-Garcia +29
This article presents SVC-onGoing, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in a…
ICDAR 2021 Competition on On-Line Signature Verification
Ruben Tolosana, Ruben Vera-Rodriguez, Carlos Gonzalez-Garcia +28
This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limi…
Towards an IMU-based Pen Online Handwriting Recognizer
Mohamad Wehbi, Tim Hamann, Jens Barth +3
Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition syste…
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
Leo Schwinn, René Raab, An Nguyen +2
Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community. Moreover, the robustness evaluat…
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis
Leo Schwinn, An Nguyen, René Raab +5
The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in sa…
System Design for a Data-driven and Explainable Customer Sentiment Monitor
An Nguyen, Stefan Foerstel, Thomas Kittler +8
The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to…
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
Leo Schwinn, An Nguyen, René Raab +4
The vulnerability of deep neural networks to small and even imperceptible perturbations has become a central topic in deep learning research. Although several sophisticated defense…
Gravitational Models Explain Shifts on Human Visual Attention
Dario Zanca, Marco Gori, Stefano Melacci +1
Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It pro…
Wave Propagation of Visual Stimuli in Focus of Attention
Lapo Faggi, Alessandro Betti, Dario Zanca +2
Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visua…
End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data
Alessandro Rossi, Sara Ermini, Dario Bernabini +4
While theories postulating a dual cognitive system take hold, quantitative confirmations are still needed to understand and identify interactions between the two systems or conflic…
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
Dario Zanca, Stefano Melacci, Marco Gori
Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient loca…
1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions
Dario Zanca, Alessandra Rufa
It is well known that a systematic analysis of the pupil size variations, recorded by means of an eye-tracker, is a rich source of information about a subject's arousal and cogniti…
Visual Attention driven by Convolutional Features
Dario Zanca, Marco Gori
The understanding of where humans look in a scene is a problem of great interest in visual perception and computer vision. When eye-tracking devices are not a viable option, models…
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
Giuseppe Marra, Dario Zanca, Alessandro Betti +1
The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles. There is also clear evidence that the activation…