Virtual Reality in Mental Health: A Self-Counselling Approach
Mel Slater, University of Barcelona, Spain
Multi-Modal Human-Machine Interaction: Joint Optimization of Single Modalities and Automatic Learning of Communication Channel Fusion
Gerhard Rigoll, Technical University of Munich, Germany
The Predictable Side of Unpredictable Humans
Alvitta Ottley, Washington University, United States
Data-centred Deep Learning Models for Food Fine-Grained Recognition
Petia Radeva, Mathematics and Computer Science, Universitat de Barcelona, Spain
Virtual Reality in Mental Health: A Self-Counselling Approach
Mel Slater
University of Barcelona
Spain
www.event-lab.org
Brief Bio
Mel Slater is a Distinguished Investigator at the University of Barcelona in the Institute of Neurosciences, and co-Director of the Event Lab (Experimental Virtual Environments for Neuroscience and Technology). He was previously Professor of Virtual Environments at University College London in the Department of Computer Science. He has been involved in research in virtual reality since the early 1990s, and has been first supervisor of 40 PhDs in graphics and virtual reality since 1989. He held a European Research Council Advanced Grant TRAVERSE 2009-2015 and has now a second Advanced Grant MoTIVE 2018-2023. He is a Research Award Winner of the Alexander von Humboldt Foundation in 2021, and was elected to the IEEE VGTC Virtual Reality Academy in 2022. He is Field Editor of Frontiers in Virtual Reality, and Chief Editor of the Human Behaviour in Virtual Reality section. His publications can be seen on http://publicationslist.org/melslater.
Abstract
Extensive research into virtual reality and its applications started in the 1990s. To date there have been over 1,700,000 scientific publications and patents that mention the terms “virtual reality”, and about 5% of these specifically include the term “mental health”. Early work concentrated on specific phobias such as fear of heights and flying, and then expanded into social phobia and general anxiety disorders, and more complex syndromes such as depression. It has most commonly been used in research in the context of exposure and cognitive behavioural therapy. The evidence suggests that the results are at least as good as conventional in vivo treatment. VR has also been used in the study and treatment of psychotic illnesses such as paranoia. In this talk I will review research in this field and move on to discuss a particular paradigm that makes use of VR for self-counselling, including its role in helping people to overcome obesity.
Multi-Modal Human-Machine Interaction: Joint Optimization of Single Modalities and Automatic Learning of Communication Channel Fusion
Gerhard Rigoll
Technical University of Munich
Germany
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Brief Bio
Gerhard Rigoll received the Dr.-Ing. degree in 1986 in the area of automatic speech recognition from Stuttgart University / Germany. From 1986 to 1988 he worked as postdoctoral fellow at IBM T.J. Watson Research Center in Yorktown Heights/USA on acoustic modeling and speaker adaptation for the IBM Tangora speech recognition system. From 1991 to 1993 he worked as guest researcher in the framework of the EC Scientific Training Programme in Japan for the NTT Human Interface Laboratories in Tokyo/Japan, in the area of neural networks and hybrid speech recognition systems. In 1993 he was appointed to full professor of computer science at Gerhard-Mercator-University in Duisburg, Germany and joined TU Munich (TUM) in 2002, where he is now heading the institute for Human-Machine Communication. His research interests are in the field of pattern recognition and machine learning for human-machine communication, covering areas such as speech and handwriting recognition, gesture recognition, face detection & identification, action & emotion recognition and interactive computer graphics. Dr. Rigoll is an IEEE Fellow (for contributions to multimodal human-machine communication) and is the author and co-author of more than 550 papers covering the above mentioned application areas. He served as reviewer for many scientific journals, and has been session chairman and program committee member for numerous international conferences. He has been also involved in international research and teaching activities as visiting professor at NAIST in Nara / Japan (2005) and as lecturer at TUM-Asia in Singapore since 2011. He is coordinator for the electrical engineering section of the Chinese-German College for Postgraduate Studies (CDHK) at Tongji-University in Shanghai / China since 2017.
Abstract
In multi-modal human-machine communication, users interact with machines using different human communication channels, such as e.g. voice, vision or haptics. It is therefore not amazing that human-machine communication benefited strongly from the extremely dynamic development of advanced machine learning methods during the last decade, since they were the driving factors in most classical pattern recognition areas, such as speech and emotion recognition, or computer vision.
In this talk, some of the recent research outcomes from the author's institution will be introduced, including e.g. face recognition from partial and occluded face information, recognition for low resolution face images, or action recognition including gait identification with graph neural networks. The talk will end with the presentation of a multi-modal recognition task for a multi-party speaker activity detection scenario where advanced deep learning methods are not only employed for single modality recognition but especially for the fusion of audio-visual information to solve a real multi-modal complex recognition problem. This approach contributes to the future perspective of human-machine communication, namely to employ advanced machine learning methods to jointly optimize the recognition components for the different modalities as well as automatically learning the strategies for their fusion, to create truly multi-modal interactive systems.
The Predictable Side of Unpredictable Humans
Alvitta Ottley
Washington University
United States
https://alvitta.com
Brief Bio
Dr. Alvitta Ottley is an Associate Professor in the Computer Science & Engineering Department at Washington University in St. Louis, Missouri, USA. She also holds a courtesy appointment in the Psychological and Brain Sciences Department. Her research uses interdisciplinary approaches to solve problems such as how best to display information for effective decision-making and how to design human-in-the-loop visual analytics interfaces that are more attuned to how people think. Dr. Ottley received an NSF CRII Award in 2018 for using visualization to support medical decision-making, the NSF Career Award for creating context-aware visual analytics systems, and the 2022 EuroVis Early Career Award. In addition, her work has appeared in leading conferences and journals such as CHI, VIS, and TVCG, achieving the best paper and honorable mention awards.
Abstract
Building AI systems that interact naturally with humans presents a tremendous challenge. This is because humans are not simply logic machines; our emotions, experiences, and social contexts all influence our behavior in unpredictable ways. So how can we, as AI developers, navigate this complexity and design systems that respond intelligently to their human counterparts? In this talk, I discuss how we approach this problem in the context of Visual Analytics by embracing the parallels between human reasoning and AI models. For example, this talk will demonstrate how humans, like AI systems, can be modeled as a set of rules and parameters, which facilitates predicting behavioral outcomes *in specific scenarios*. By employing inferential techniques like Bayesian reasoning, we will go beyond actions and discuss how we might model and infer deeper motivations and beliefs driving them. Truly understanding humans is non-trivial, but we can forge a path toward effective human-AI interaction by leveraging the similarities between human thought processes and AI modeling.
Data-centred Deep Learning Models for Food Fine-Grained Recognition
Petia Radeva
Mathematics and Computer Science, Universitat de Barcelona
Spain
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Brief Bio
Prof. Petia Radeva is a Full professor at the Universitat de Barcelona (UB), Head of the Consolidated Research Group “Artificial Intelligence and Biomedical Applications (AIBA)” at the University of Barcelona. Her main interests are in Machine/Deep learning and Computer Vision and their applications to health. Specific topics of interest: data-centric deep learning, uncertainty modeling, self-supervised learning, continual learning, learning with noisy labeling, multi-modal learning, NeRF, food recognition, food ontology, etc. She is an Associate editor in Chief of Pattern Recognition journal and International Journal of Visual Communication and Image Representation. She is a Research Manager of the State Agency of Research (Agencia Estatal de Investigación, AEI) of the Ministry of Science and Innovation of Spain. She supervised 24 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings. Petia Radeva belongs to the top 2% of the World ranking of scientists with the major impact in the field of TIC according to the citations indicators of the popular ranking of Stanford. Moreover, she was awarded IAPR Fellow since 2015, ICREA Academia’2015 and ICREA Academia’2022 assigned to the 30 best scientists in Catalonia for her scientific merits, received several international and national awards (“Aurora Pons Porrata”, Prize “Antonio Caparrós” ).
Abstract
To be announced.