Beyond deep learning: charting the next frontiers of affective computing

  • Affective computing (AC), like most other areas of computational research, has benefited tremendously from advances in deep learning (DL). These advances have opened up new horizons in AC research and practice. Yet, as DL dominates the community’s attention, there is a danger of overlooking other emerging trends in artificial intelligence (AI) research. Furthermore, over-reliance on one particular technology may lead to stagnating progress. In an attempt to foster the exploration of complementary directions, we provide a concise, easily digestible overview of emerging trends in AI research that stand to play a vital role in solving some of the remaining challenges in AC research. Our overview is driven by the limitations of the current state of the art as it pertains to AC.

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Metadaten
Author:Andreas TriantafyllopoulosORCiD, Lukas Christ, Alexander Gebhard, Xin Jing, Alexander KathanORCiD, Manuel MillingGND, Iosif Tsangko, Shahin AmiriparianORCiDGND, Björn W. SchullerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1180798
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/118079
ISSN:2771-5892OPAC
Parent Title (English):Intelligent Computing
Publisher:American Association for the Advancement of Science (AAAS)
Place of publication:Washington, D.C.
Type:Article
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2025/01/19
Volume:3
First Page:0089
DOI:https://doi.org/10.34133/icomputing.0089
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)