Search Results for author: Elisabeth André

Found 41 papers, 8 papers with code

Unimodal Multi-Task Fusion for Emotional Mimicry Prediction

no code implementations18 Mar 2024 Tobias Hallmen, Fabian Deuser, Norbert Oswald, Elisabeth André

In this study, we propose a methodology for the Emotional Mimicry Intensity (EMI) Estimation task within the context of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild.

The AffectToolbox: Affect Analysis for Everyone

no code implementations23 Feb 2024 Silvan Mertes, Dominik Schiller, Michael Dietz, Elisabeth André, Florian Lingenfelser

In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent.

Emotion Recognition

Exploring the Dynamics between Cobot's Production Rhythm, Locus of Control and Emotional State in a Collaborative Assembly Scenario

no code implementations1 Feb 2024 Marta Mondellini, Matteo Lavit Nicora, Pooja Prajod, Elisabeth André, Rocco Vertechy, Alessandro Antonietti, Matteo Malosio

In industrial scenarios, there is widespread use of collaborative robots (cobots), and growing interest is directed at evaluating and measuring the impact of some characteristics of the cobot on the human factor.

Gaze Detection and Analysis for Initiating Joint Activity in Industrial Human-Robot Collaboration

no code implementations11 Dec 2023 Pooja Prajod, Matteo Lavit Nicora, Marta Mondellini, Giovanni Tauro, Rocco Vertechy, Matteo Malosio, Elisabeth André

To the best of our knowledge, this is the first study to analyze the natural gaze behavior of participants working on a joint activity with a robot during a collaborative assembly task.

Fostering User Engagement in the Critical Reflection of Arguments

no code implementations17 Aug 2023 Klaus Weber, Annalena Aicher, Wolfang Minker, Stefan Ultes, Elisabeth André

To support a fair and unbiased opinion-building process, we propose a chatbot system that engages in a deliberative dialogue with a human.


Towards Automated COVID-19 Presence and Severity Classification

no code implementations15 May 2023 Dominik Müller, Niklas Schröter, Silvan Mertes, Fabio Hellmann, Miriam Elia, Wolfgang Reif, Bernhard Bauer, Elisabeth André, Frank Kramer

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times.

Classification Ensemble Learning +2

GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions

1 code implementation3 May 2023 Fabio Hellmann, Silvan Mertes, Mohamed Benouis, Alexander Hustinx, Tzung-Chien Hsieh, Cristina Conati, Peter Krawitz, Elisabeth André

The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face.

Face Anonymization Generative Adversarial Network

Gaze-based Attention Recognition for Human-Robot Collaboration

no code implementations30 Mar 2023 Pooja Prajod, Matteo Lavit Nicora, Matteo Malosio, Elisabeth André

We performed an additional validation of our models using the video snippets collected from participants working as an operator in the presented assembly scenario.

Transfer Learning

GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations

2 code implementations24 Feb 2023 Tobias Huber, Maximilian Demmler, Silvan Mertes, Matthew L. Olson, Elisabeth André

However, research focusing on counterfactual explanations, specifically for RL agents with visual input, is scarce and does not go beyond identifying defective agents.

counterfactual Decision Making +2

Around the world in 60 words: A generative vocabulary test for online research

no code implementations3 Feb 2023 Pol van Rijn, Yue Sun, Harin Lee, Raja Marjieh, Ilia Sucholutsky, Francesca Lanzarini, Elisabeth André, Nori Jacoby

Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable ($r=0. 82$), and (c) performance strongly correlates with existing tests (LexTale) and self-reports.

Cultural Vocal Bursts Intensity Prediction

Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents

no code implementations21 Oct 2022 Yael Septon, Tobias Huber, Elisabeth André, Ofra Amir

Methods that help users understand the behavior of such agents can roughly be divided into local explanations that analyze specific decisions of the agents and global explanations that convey the general strategy of the agents.

Decision Making reinforcement-learning +1

On the Generalizability of ECG-based Stress Detection Models

no code implementations12 Oct 2022 Pooja Prajod, Elisabeth André

To the best of our knowledge, this is the first work to compare the cross-dataset generalizability between ECG-based deep learning models and HRV models.

Heart Rate Variability

What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps

no code implementations7 Oct 2022 Katharina Weitz, Alexander Zellner, Elisabeth André

In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical application.

Explainable Artificial Intelligence (XAI)

Do We Need Explainable AI in Companies? Investigation of Challenges, Expectations, and Chances from Employees' Perspective

no code implementations7 Oct 2022 Katharina Weitz, Chi Tai Dang, Elisabeth André

By providing insights into employees' needs and attitudes towards (X)AI, our project report contributes to the development of XAI solutions that meet the requirements of companies and their employees, ultimately driving the successful adoption of AI technologies in the business context.

Explainable Artificial Intelligence (XAI) Management

Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems

no code implementations19 Jul 2022 Silvan Mertes, Christina Karle, Tobias Huber, Katharina Weitz, Ruben Schlagowski, Elisabeth André

We evaluate our approach in an extensive user study, revealing that it is able to significantly contribute to the participants' understanding of an AI.

counterfactual Counterfactual Explanation +2

Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?

no code implementations3 Jul 2022 Mani Kumar Tellamekala, Ömer Sümer, Björn W. Schuller, Elisabeth André, Timo Giesbrecht, Michel Valstar

We also study how 3D face shapes performed on AU intensity estimation on BP4D and DISFA datasets, and report that 3D face features were on par with 2D appearance features in AUs 4, 6, 10, 12, and 25, but not the entire set of AUs.

3D Face Alignment Arousal Estimation +1

Employing Socially Interactive Agents for Robotic Neurorehabilitation Training

no code implementations3 Jun 2022 Rhythm Arora, Matteo Lavit Nicora, Pooja Prajod, Daniele Panzeri, Elisabeth André, Patrick Gebhard, Matteo Malosio

In today's world, many patients with cognitive impairments and motor dysfunction seek the attention of experts to perform specific conventional therapies to improve their situation.

Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning

no code implementations9 May 2022 Silvan Mertes, Andreas Margraf, Steffen Geinitz, Elisabeth André

However, a precise examination of the resulting images indicate that WGAN and image-to-image translation achieve good segmentation results and only deviate to a small degree from traditional data augmentation.

Data Augmentation Image-to-Image Translation +2

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

no code implementations19 Aug 2021 Tobias Huber, Silvan Mertes, Stanislava Rangelova, Simon Flutura, Elisabeth André

As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network.

Benchmarking Perturbation-based Saliency Maps for Explaining Atari Agents

1 code implementation18 Jan 2021 Tobias Huber, Benedikt Limmer, Elisabeth André

One of the most prominent methods for explaining the behavior of Deep Reinforcement Learning (DRL) agents is the generation of saliency maps that show how much each pixel attributed to the agents' decision.

Atari Games Benchmarking +2

Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

1 code implementation18 May 2020 Tobias Huber, Katharina Weitz, Elisabeth André, Ofra Amir

Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to.

Atari Games Decision Making +3

Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora

1 code implementation7 Feb 2018 Johannes Wagner, Tobias Baur, Yue Zhang, Michel F. Valstar, Björn Schuller, Elisabeth André

Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour.

Interpreting social cues to generate credible affective reactions of virtual job interviewers

no code implementations20 Feb 2014 Hazael Jones, Nicolas Sabouret, Ionut Damian, Tobias Baur, Elisabeth André, Kaśka Porayska-Pomsta, Paola Rizzo

In this paper we describe a mechanism of generating credible affective reactions in a virtual recruiter during an interaction with a user.

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