arXiv subjects

Elisabeth André

Publications and source records attributed to Elisabeth André.

At least 55 records · Page 3Linked to original sources

arXiv2023

MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions

Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact with- and support humans in social interactions. In MultiMediate'23, we address two key human social behaviour analysis tasks for the first time in a controlled challenge: engagement estimation and bodily behaviour recognition in social interactions. This paper describes the MultiMediate'23 challenge and presents novel sets of annotations for both tasks. For engagement estimation we collected novel annotations on the NOvice eXpert Interaction (NOXI) database. For bodily behaviour recognition, we annotated test recordings of the MPIIGroupInteraction corpus with the BBSI annotation scheme. In addition, we present baseline results for both challenge tasks.

cs.CV↗

arXiv2023

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

Companies' adoption of artificial intelligence (AI) is increasingly becoming an essential element of business success. However, using AI poses new requirements for companies and their employees, including transparency and comprehensibility of AI systems. The field of Explainable AI (XAI) aims to address these issues. Yet, the current research primarily consists of laboratory studies, and there is a need to improve the applicability of the findings to real-world situations. Therefore, this project report paper provides insights into employees' needs and attitudes towards (X)AI. For this, we investigate employees' perspectives on (X)AI. Our findings suggest that AI and XAI are well-known terms perceived as important for employees. This recognition is a critical first step for XAI to potentially drive successful usage of AI by providing comprehensible insights into AI technologies. In a lessons-learned section, we discuss the open questions identified and suggest future research directions to develop human-centered XAI designs for companies. 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.

cs.HC↗

arXiv2023

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

Recognising continuous emotions and action unit (AU) intensities from face videos requires a spatial and temporal understanding of expression dynamics. Existing works primarily rely on 2D face appearances to extract such dynamics. This work focuses on a promising alternative based on parametric 3D face shape alignment models, which disentangle different factors of variation, including expression-induced shape variations. We aim to understand how expressive 3D face shapes are in estimating valence-arousal and AU intensities compared to the state-of-the-art 2D appearance-based models. We benchmark four recent 3D face alignment models: ExpNet, 3DDFA-V2, DECA, and EMOCA. In valence-arousal estimation, expression features of 3D face models consistently surpassed previous works and yielded an average concordance correlation of .739 and .574 on SEWA and AVEC 2019 CES corpora, respectively. 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. To understand this discrepancy, we conduct a correspondence analysis between valence-arousal and AUs, which points out that accurate prediction of valence-arousal may require the knowledge of only a few AUs.

cs.CV↗

arXiv2023

Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders

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 to affect faces' visual appearance and geometry. Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem and used deep learning methods. The challenging issue in practice is the sparse label distribution and huge class imbalances across categories. Furthermore, most disorders have few labeled samples in training sets, making representation learning and generalization essential to acquiring a reliable feature descriptor. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition. Furthermore, we created simple baselines of few-shot meta-learning methods to improve our base feature descriptor. Our quantitative results on GestaltMatcher Database show that our CNN baseline surpasses previous works, including GestaltMatcher, and few-shot meta-learning strategies improve retrieval performance in frequent and rare classes.

cs.CV↗

arXiv2023

Towards Automated COVID-19 Presence and Severity Classification

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.

eess.IV↗

arXiv2023

Gaze-based Attention Recognition for Human-Robot Collaboration

Attention (and distraction) recognition is a key factor in improving human-robot collaboration. We present an assembly scenario where a human operator and a cobot collaborate equally to piece together a gearbox. The setup provides multiple opportunities for the cobot to adapt its behavior depending on the operator's attention, which can improve the collaboration experience and reduce psychological strain. As a first step, we recognize the areas in the workspace that the human operator is paying attention to, and consequently, detect when the operator is distracted. We propose a novel deep-learning approach to develop an attention recognition model. First, we train a convolutional neural network to estimate the gaze direction using a publicly available image dataset. Then, we use transfer learning with a small dataset to map the gaze direction onto pre-defined areas of interest. Models trained using this approach performed very well in leave-one-subject-out evaluation on the small dataset. We performed an additional validation of our models using the video snippets collected from participants working as an operator in the presented assembly scenario. Although the recall for the Distracted class was lower in this case, the models performed well in recognizing the areas the operator paid attention to. To the best of our knowledge, this is the first work that validated an attention recognition model using data from a setting that mimics industrial human-robot collaboration. Our findings highlight the need for validation of attention recognition solutions in such full-fledged, non-guided scenarios.

cs.HC↗

arXiv2023

ForDigitStress: A multi-modal stress dataset employing a digital job interview scenario

We present a multi-modal stress dataset that uses digital job interviews to induce stress. The dataset provides multi-modal data of 40 participants including audio, video (motion capturing, facial recognition, eye tracking) as well as physiological information (photoplethysmography, electrodermal activity). In addition to that, the dataset contains time-continuous annotations for stress and occurred emotions (e.g. shame, anger, anxiety, surprise). In order to establish a baseline, five different machine learning classifiers (Support Vector Machine, K-Nearest Neighbors, Random Forest, Long-Short-Term Memory Network) have been trained and evaluated on the proposed dataset for a binary stress classification task. The best-performing classifier achieved an accuracy of 88.3% and an F1-score of 87.5%.

cs.LG↗

arXiv2023

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

Counterfactual explanations are a common tool to explain artificial intelligence models. For Reinforcement Learning (RL) agents, they answer "Why not?" or "What if?" questions by illustrating what minimal change to a state is needed such that an agent chooses a different action. Generating counterfactual explanations for RL agents with visual input is especially challenging because of their large state spaces and because their decisions are part of an overarching policy, which includes long-term decision-making. However, research focusing on counterfactual explanations, specifically for RL agents with visual input, is scarce and does not go beyond identifying defective agents. It is unclear whether counterfactual explanations are still helpful for more complex tasks like analyzing the learned strategies of different agents or choosing a fitting agent for a specific task. We propose a novel but simple method to generate counterfactual explanations for RL agents by formulating the problem as a domain transfer problem which allows the use of adversarial learning techniques like StarGAN. Our method is fully model-agnostic and we demonstrate that it outperforms the only previous method in several computational metrics. Furthermore, we show in a user study that our method performs best when analyzing which strategies different agents pursue.

cs.LG↗

arXiv2023

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

Conducting experiments with diverse participants in their native languages can uncover insights into culture, cognition, and language that may not be revealed otherwise. However, conducting these experiments online makes it difficult to validate self-reported language proficiency. Furthermore, existing proficiency tests are small and cover only a few languages. We present an automated pipeline to generate vocabulary tests using text from Wikipedia. Our pipeline samples rare nouns and creates pseudowords with the same low-level statistics. 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. We further show that test accuracy is negatively correlated with the linguistic distance between the tested and the native language. Our test, available in eight languages, can easily be extended to other languages.

cs.CL↗

arXiv2023

Wish You Were Here: Mental and Physiological Effects of Remote Music Collaboration in Mixed Reality

With face-to-face music collaboration being severely limited during the recent pandemic, mixed reality technologies and their potential to provide musicians a feeling of "being there" with their musical partner can offer tremendous opportunities. In order to assess this potential, we conducted a laboratory study in which musicians made music together in real-time while simultaneously seeing their jamming partner's mixed reality point cloud via a head-mounted display and compared mental effects such as flow, affect, and co-presence to an audio-only baseline. In addition, we tracked the musicians' physiological signals and evaluated their features during times of self-reported flow. For users jamming in mixed reality, we observed a significant increase in co-presence. Regardless of the condition (mixed reality or audio-only), we observed an increase in positive affect after jamming remotely. Furthermore, we identified heart rate and HF/LF as promising features for classifying the flow state musicians experienced while making music together.

cs.HC↗

arXiv2023

Nichtverbales Verhalten sozialer Roboter: Bewegungen, deren Bedeutung und die Technik dahinter

Nichtverbale Signale sind ein elementarer Bestandteil der menschlichen Kommunikation. Sie erfüllen eine Vielzahl von Funktionen bei der Klärung von Mehrdeutigkeiten, der subtilen Aushandlung von Rollen oder dem Ausdruck dessen, was im Inneren der Gesprächspartner vorgeht. Viele Studien mit sozial-interaktiven Robotern zeigen, dass vom Menschen inspirierte Bewegungsmuster ähnlich interpretiert werden wie die von realen Personen. Dieses Kapitel erläutert daher die wichtigsten Funktionen, welche die jeweiligen Bewegungsmuster in der Kommunikation erfüllen, und gibt einen Überblick darüber, wie sie auf Roboter übertragen werden können. -- Non-verbal signals are a fundamental part of human communication. They serve a variety of functions in clarifying ambiguities, subtly negotiating roles, or expressing what is going on inside the interlocutors. Many studies with socially-interactive robots show that human-inspired movement patterns are interpreted similarly to those of real people. This chapter therefore explains the most important functions that the respective movement patterns fulfill in communication and gives an overview of how they can be transferred to robots.

cs.RO↗

arXiv2022

Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles

Rare genetic disorders affect more than 6% of the global population. Reaching a diagnosis is challenging because rare disorders are very diverse. Many disorders have recognizable facial features that are hints for clinicians to diagnose patients. Previous work, such as GestaltMatcher, utilized representation vectors produced by a DCNN similar to AlexNet to match patients in high-dimensional feature space to support "unseen" ultra-rare disorders. However, the architecture and dataset used for transfer learning in GestaltMatcher have become outdated. Moreover, a way to train the model for generating better representation vectors for unseen ultra-rare disorders has not yet been studied. Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them. Therefore, we first analyzed the influence of replacing GestaltMatcher DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Additionally, we experimented with different face recognition datasets for transfer learning. Furthermore, we proposed test-time augmentation, and model ensembles that mix general face verification models and models specific for verifying disorders to improve the disorder verification accuracy of unseen ultra-rare disorders. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.

cs.CV↗

arXiv2022

Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents

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 delayed rewards. 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. In this work, we study a novel combination of local and global explanations for reinforcement learning agents. Specifically, we combine reward decomposition, a local explanation method that exposes which components of the reward function influenced a specific decision, and HIGHLIGHTS, a global explanation method that shows a summary of the agent's behavior in decisive states. We conducted two user studies to evaluate the integration of these explanation methods and their respective benefits. Our results show significant benefits for both methods. In general, we found that the local reward decomposition was more useful for identifying the agents' priorities. However, when there was only a minor difference between the agents' preferences, then the global information provided by HIGHLIGHTS additionally improved participants' understanding.

cs.LG↗

arXiv2022

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

In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical application. Overcoming this barrier is the goal of explainable AI (XAI). However, an explanation can be perceived differently and, thus, not solve the black-box problem for everyone. The domain of Human-Centered AI deals with this problem by adapting AI to users. We present a user-centered persona concept to evaluate XAI and use it to investigate end-users preferences for various explanation styles and contents in a mobile health stress monitoring application. The results of our online survey show that users' demographics and personality, as well as the type of explanation, impact explanation preferences, indicating that these are essential features for XAI design. We subsumed the results in three prototypical user personas: power-, casual-, and privacy-oriented users. Our insights bring an interactive, human-centered XAI closer to practical application.

cs.HC↗

arXiv2022

An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era

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 managed to master the art of generating speech that is understandable by humans. But the linguistic content of an utterance encompasses only a part of its meaning. Affect, or expressivity, has the capacity to turn speech into a medium capable of conveying intimate thoughts, feelings, and emotions -- aspects that are essential for engaging and naturalistic interpersonal communication. While the goal of imparting expressivity to synthesised utterances has so far remained elusive, following recent advances in text-to-speech synthesis, a paradigm shift is well under way in the fields of affective speech synthesis and conversion as well. Deep learning, as the technology which underlies most of the recent advances in artificial intelligence, is spearheading these efforts. In the present overview, we outline ongoing trends and summarise state-of-the-art approaches in an attempt to provide a comprehensive overview of this exciting field.

cs.SD↗

arXiv2022

An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis

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 or sighs. Current state-of-the-art approaches to address affective vocal burst analysis are mostly based on wav2vec2 or HuBERT features. In this paper, we investigate the use of the wav2vec successor data2vec in combination with a multitask learning pipeline to tackle different analysis problems at once. To assess the performance of our efficient multitask learning architecture, we participate in the 2022 ACII Affective Vocal Burst Challenge, showing that our approach substantially outperforms the baseline established there in three different subtasks.

cs.SD↗

arXiv2022

MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions

Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroupInteraction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.

cs.HC↗

arXiv2022

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

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 that humans are familiar with. However, all common approaches from this field are based on communicating information about features or characteristics that are especially important for an AI's decision. We argue that in order to fully understand a decision, not only knowledge about relevant features is needed, but that the awareness of irrelevant information also highly contributes to the creation of a user's mental model of an AI system. Therefore, we introduce a new way of explaining AI systems. Our approach, which we call Alterfactual Explanations, is based on showing an alternative reality where irrelevant features of an AI's input are altered. By doing so, the user directly sees which characteristics of the input data can change arbitrarily without influencing the AI's decision. We evaluate our approach in an extensive user study, revealing that it is able to significantly contribute to the participants' understanding of an AI. We show that alterfactual explanations are suited to convey an understanding of different aspects of the AI's reasoning than established counterfactual explanation methods.

cs.AI↗