Search Results for author:
Found 57 papers, 16 papers with code
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT
Our findings provide insights into using advanced, multimodal techniques for automated classroom observation, aiming to foster teacher training through frequent and valuable feedback.
Pretrained Visual Uncertainties
Similar to standard pretraining this enables the zero-shot transfer of uncertainties learned on a large pretraining dataset to specialized downstream datasets.
Stepwise Self-Consistent Mathematical Reasoning with Large Language Models
SSC-CoT employs a strategy of selecting intermediate steps based on the intersection of various reasoning chains.
Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy
Lastly, we speculate that combining the information provided to LLM-powered environments by the users and the biometric data obtained through the sensors might lead to novel privacy invasions.
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years.
Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education
The integration of Artificial Intelligence (AI), particularly Large Language Model (LLM)-based systems, in education has shown promise in enhancing teaching and learning experiences.
I-CEE: Tailoring Explanations of Image Classification Models to User Expertise
Informed by existing work, I-CEE explains the decisions of image classification models by providing the user with an informative subset of training data (i. e., example images), corresponding local explanations, and model decisions.
Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)
The advent of foundation models signals a new era in artificial intelligence.
LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images
This problem is exacerbated by both hardware-induced variations in eye images and inherent biological differences across the recorded participants, leading to both feature and pixel-level variance that hinders the generalizability of models trained on specific datasets.
Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters
The AI system can accurately identify many fundamental student errors, for instance, the AI system identifies when a student is focusing the hypothesis not on the dependent variable but solely on an expected observation (acc.
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain.
Efficient GNN Explanation via Learning Removal-based Attribution
A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process.
DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection
In this work, we propose a novel feature fusion strategy, DynStaF (Dynamic-Static Fusion), which enhances the rich semantic information provided by the multi-frame (dynamic branch) with the accurate location information from the current single-frame (static branch).
Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges
Latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life.
Precise localization of corneal reflections in eye images using deep learning trained on synthetic data
Our method outperformed state-of-the-art algorithmic methods on real eye images with a 35% reduction in terms of spatial precision, and performed on par with state-of-the-art on simulated images in terms of spatial accuracy. We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem which is one of the important common roadblocks in the development of deep learning models for gaze estimation.
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
We prove that these distributions recover the correct posteriors of the data-generating process, including its level of aleatoric uncertainty, up to a rotation of the latent space.
Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge.
Explainable Artificial Intelligence (XAI)
Explainable Models
+2
Deep Learning-Based Position Detection for Hydraulic Cylinders Using Scattering Parameters
A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston position based on the scattering parameters measured by a sensor.
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning
We model images as directional von Mises-Fisher (vMF) distributions on the hypersphere that can reflect image-intrinsic uncertainties.
When are Post-hoc Conceptual Explanations Identifiable?
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing.
Where and What: Driver Attention-based Object Detection
Human drivers use their attentional mechanisms to focus on critical objects and make decisions while driving.
Gaze-based Object Detection in the Wild
For this purpose, we explore different sizes of temporal windows, which serve as a basis for the computation of heatmaps, i. e., the spatial distribution of the gaze data.
A Consistent and Efficient Evaluation Strategy for Attribution Methods
With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies.
Human Attention in Fine-grained Classification
The way humans attend to, process and classify a given image has the potential to vastly benefit the performance of deep learning models.
Ranked #41 on
Fine-Grained Image Classification
on CUB-200-2011
Robust Deep Neural Networks for Heterogeneous Tabular Data
Although deep neural networks (DNNs) constitute the state-of-the-art in many tasks based on image, audio, or text data, their performance on heterogeneous, tabular data is typically inferior to that of decision tree ensembles.
States of confusion: Eye and Head tracking reveal surgeons' confusion during arthroscopic surgery
During arthroscopic surgeries, surgeons are faced with challenges like cognitive re-projection of the 2D screen output into the 3D operating site or navigation through highly similar tissue.
Estimating Presentation Competence using Multimodal Nonverbal Behavioral Cues
One approach that can promote efficient development of presentation competence is the automated analysis of human behavior during a speech based on visual and audio features and machine learning.
Differentiating Surgeon Expertise Solely by Eye Movement Features
In this study, we present a model for classifying experts, 4th-year residents and 3rd-year residents, using only eye movements.
TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types
The data set includes 2D and 3D landmarks, semantic segmentation, 3D eyeball annotation and the gaze vector and eye movement types for all images.
A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis
We present a new dataset with annotated eye movements.
Multimodal Engagement Analysis from Facial Videos in the Classroom
To best examine student visual engagement in the classroom, we conducted a study utilizing the audiovisual recordings of classes at a secondary school over one and a half month's time, acquired continuous engagement labeling per student (N=15) in repeated sessions, and explored computer vision methods to classify engagement levels from faces in the classroom.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Artificial Intelligence Methods in In-Cabin Use Cases: A Survey
As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles.
Explainable Online Validation of Machine Learning Models for Practical Applications
The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.
Rotated Ring, Radial and Depth Wise Separable Radial Convolutions
We also discuss the influence of purely rotational invariant features on accuracy.
Weight and Gradient Centralization in Deep Neural Networks
Batch normalization is currently the most widely used variant of internal normalization for deep neural networks.
Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm
A concluding analysis of the best performing feature sets yields valuable insights about the influence of drowsiness on the driver's blink behavior and head movements.
Eye Movement Feature Classification for Soccer Goalkeeper Expertise Identification in Virtual Reality
The latest research in expertise assessment of soccer players has affirmed the importance of perceptual skills (especially for decision making) by focusing either on high experimental control or on a realistic presentation.
Driver Intention Anticipation Based on In-Cabin and Driving SceneMonitoring
Numerous car accidents are caused by improper driving maneuvers.
Multi Layer Neural Networks as Replacement for Pooling Operations
This kind of pooling allows for the integration of multi-layer neural networks directly into a model as a pooling operation by restructuring the data and, as a result, learnin complex pooling operations.
Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing
Modeling eye movement indicative of expertise behavior is decisive in user evaluation.
Differential Privacy for Eye Tracking with Temporal Correlations
New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications.
Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction
In this paper, we use fully convolutional neural networks for the semantic segmentation of eye tracking data.
Reinforcement learning for the privacy preservation and manipulation of eye tracking data
In this paper, we present an approach based on reinforcement learning for eye tracking data manipulation.
Automated Anonymisation of Visual and Audio Data in Classroom Studies
A possible solution would be to request permission to record the audio-visual recordings of all students (including those who do not voluntarily participate in the study) and to anonymise their data.
Attention Flow: End-to-End Joint Attention Estimation
This paper addresses the problem of understanding joint attention in third-person social scene videos.
Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework
Eye tracking is handled as one of the key technologies for applications that assess and evaluate human attention, behavior, and biometrics, especially using gaze, pupillary, and blink behaviors.
Training Decision Trees as Replacement for Convolution Layers
Our approach reduces the complexity of convolutions by replacing it with binary decisions.
Learning to Validate the Quality of Detected Landmarks
In addition, we evaluated the impact of the validation loss on the landmark accuracy based on uniform sampling.
Teacher's Perception in the Classroom
The ability for a teacher to engage all students in active learning processes in classroom constitutes a crucial prerequisite for enhancing students achievement.
Eye movement simulation and detector creation to reduce laborious parameter adjustments
Eye movements hold information about human perception, intention and cognitive state.
PuRe: Robust pupil detection for real-time pervasive eye tracking
state-of-the-art algorithms by 25. 05 and 10. 94 percentage points, respectively, demonstrating the meaningfulness of PuRe's confidence measure.
Fast camera focus estimation for gaze-based focus control
Many cameras implement auto-focus functionality.
PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection
Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking.
PupilNet: Convolutional Neural Networks for Robust Pupil Detection
Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking.
Bayesian Identification of Fixations, Saccades, and Smooth Pursuits
Smooth pursuit eye movements provide meaningful insights and information on subject's behavior and health and may, in particular situations, disturb the performance of typical fixation/saccade classification algorithms.
Rendering refraction and reflection of eyeglasses for synthetic eye tracker images
While for the evaluation of robustness of eye tracking algorithms the use of real-world data is essential, there are many applications where simulated, synthetic eye images are of advantage.
ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments
Fast and robust pupil detection is an essential prerequisite for video-based eye-tracking in real-world settings.
