110 citations · 377 across the 25 of their papers we have counts for
25 papers
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Yael Septon, Tobias Huber, Elisabeth André +1
Explaining the behavior of reinforcement learning agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and…
What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps
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 applicati…
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Andreas Triantafyllopoulos, Björn W. Schuller, Gökçe İymen +9
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research. In recent years, machines have manage…
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders
Ömer Sümer, Fabio Hellmann, Alexander Hustinx +3
Computer vision-based methods have valuable use cases in precision medicine, and recognizing facial phenotypes of genetic disorders is one of them. Many genetic disorders are known…
On the Generalizability of ECG-based Stress Detection Models
Pooja Prajod, Elisabeth André
Stress is prevalent in many aspects of everyday life including work, healthcare, and social interactions. Many works have studied handcrafted features from various bio-signals that…
Do We Need Explainable AI in Companies? Investigation of Challenges, Expectations, and Chances from Employees' Perspective
Katharina Weitz, Chi Tai Dang, Elisabeth André
Companies' adoption of artificial intelligence (AI) is increasingly becoming an essential element of business success. However, using AI poses new requirements for companies and th…
An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis
Tobias Hallmen, Silvan Mertes, Dominik Schiller +1
Affective speech analysis is an ongoing topic of research. A relatively new problem in this field is the analysis of vocal bursts, which are nonverbal vocalisations such as laughs…
MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions
Philipp Müller, Michael Dietz, Dominik Schiller +5
Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, auto…
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems
Silvan Mertes, Christina Karle, Tobias Huber +3
Explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning…
Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?
Mani Kumar Tellamekala, Ömer Sümer, Björn W. Schuller +3
Recognising continuous emotions and action unit (AU) intensities from face videos requires a spatial and temporal understanding of expression dynamics. Existing works primarily rel…
"GAN I hire you?" -- A System for Personalized Virtual Job Interview Training
Alexander Heimerl, Silvan Mertes, Tanja Schneeberger +6
Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. Professional job interview trainers give…
Employing Socially Interactive Agents for Robotic Neurorehabilitation Training
Rhythm Arora, Matteo Lavit Nicora, Pooja Prajod +4
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…
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition
Mani Kumar Tellamekala, Shahin Amiriparian, Björn W. Schuller +3
Automatically recognising apparent emotions from face and voice is hard, in part because of various sources of uncertainty, including in the input data and the labels used in a mac…
Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning
Silvan Mertes, Andreas Margraf, Steffen Geinitz +1
Visual inspection software has become a key factor in the manufacturing industry for quality control and process monitoring. Semantic segmentation models have gained importance sin…
Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks
Silvan Mertes, Dominik Schiller, Florian Lingenfelser +4
Generative adversarial networks offer the possibility to generate deceptively real images that are almost indistinguishable from actual photographs. Such systems however rely on th…
VoiceMe: Personalized voice generation in TTS
Pol van Rijn, Silvan Mertes, Dominik Schiller +5
Novel text-to-speech systems can generate entirely new voices that were not seen during training. However, it remains a difficult task to efficiently create personalized voices fro…
Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation
Tobias Huber, Silvan Mertes, Stanislava Rangelova +2
Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive…
Exploring emotional prototypes in a high dimensional TTS latent space
Pol van Rijn, Silvan Mertes, Dominik Schiller +4
Recent TTS systems are able to generate prosodically varied and realistic speech. However, it is unclear how this prosodic variation contributes to the perception of speakers' emot…
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition
Pooja Prajod, Dominik Schiller, Tobias Huber +1
In this paper, we present a process to investigate the effects of transfer learning for automatic facial expression recognition from emotions to pain. To this end, we first train a…
Benchmarking Perturbation-based Saliency Maps for Explaining Atari Agents
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 attribute…
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning
Silvan Mertes, Tobias Huber, Katharina Weitz +2
With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especi…
Towards Somaesthetics Inspired Games: Exploring the Influence of a Mirror Effect on Self-Presentation in a Public Setting
Fiona Guerin, Alice Rey, Enis Caliskan +4
We report on an initial user study, which explores how players of an augmented mirror game, self-style or self-present themselves when they are allowed to see themselves in the mir…
Drawing with AI -- Exploring Collaborative Inking Experiences Based on Mid-air Pointing and Reinforcement Learning
Franziska Geiger, Michelle Martin, Monika Pichlmair +4
Digitalization is changing the nature of tools and materials, which are used in artistic practices in professional and non-professional settings. For example, today it is common th…
Embedded Emotions -- A Data Driven Approach to Learn Transferable Feature Representations from Raw Speech Input for Emotion Recognition
Dominik Schiller, Silvan Mertes, Elisabeth André
Traditional approaches to automatic emotion recognition are relying on the application of handcrafted features. More recently however the advent of deep learning enabled algorithms…