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Found 28 papers, 11 papers with code
Trends, Applications, and Challenges in Human Attention Modelling
Human attention modelling has proven, in recent years, to be particularly useful not only for understanding the cognitive processes underlying visual exploration, but also for providing support to artificial intelligence models that aim to solve problems in various domains, including image and video processing, vision-and-language applications, and language modelling.
From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations
In this paper, we propose a novel auxiliary pretraining method that is based on spatial reasoning.
Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors
Understanding the mechanisms underlying human attention is a fundamental challenge for both vision science and artificial intelligence.
Ranked #1 on
Scanpath prediction
on CapMIT1003
FastAMI -- a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
Clustering is at the very core of machine learning, and its applications proliferate with the increasing availability of data.
Simulating Human Gaze with Neural Visual Attention
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.
Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
We also demonstrate that our family of models is able to generalize well towards larger scales and improve scale equivariance.
Active Learning of Ordinal Embeddings: A User Study on Football Data
We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples.
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
The reliability of neural networks is essential for their use in safety-critical applications.
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
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.
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
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.
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series
Explanatory visual interpretation approaches for image, and natural language processing allow domain experts to validate and understand almost any deep learning model.
Objective Evaluation of Deep Visual Interpretations on Time Series Data
(Explanatory) visual interpretation approaches for image and natural language processing allow domain experts to validate and understand almost any deep learning model.
SVC-onGoing: Signature Verification Competition
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 an open common platform using large-scale public databases, such as DeepSignDB and SVC2021_EvalDB, and standard experimental protocols.
ICDAR 2021 Competition on On-Line Signature Verification
This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021).
Towards an IMU-based Pen Online Handwriting Recognizer
Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data.
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community.
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis
The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in safety-critical applications.
System Design for a Data-driven and Explainable Customer Sentiment Monitor
The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices.
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
We empirically show that by incorporating this nonlocal gradient information, we are able to give a more accurate estimation of the global descent direction on noisy and non-convex loss surfaces.
Gravitational Models Explain Shifts on Human Visual Attention
Another where the information from these maps is merged in order to select a single location to be attended for further and more complex computations and reasoning.
Wave Propagation of Visual Stimuli in Focus of Attention
Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field.
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics).
1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions
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 cognitive state.
End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data
While theories postulating a dual cognitive system take hold, quantitative confirmations are still needed to understand and identify interactions between the two systems or conflict events.
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles.
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity
The second reason is the lack of sufficiently large and varied scanpath datasets.
Variational Laws of Visual Attention for Dynamic Scenes
We devise variational laws of the eye-movement that rely on a generalized view of the Least Action Principle in physics.
Ranked #1 on
Saliency Detection
on CAT2000
Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow
This report provides an introduction to some Machine Learning tools within the most common development environments.
