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Beyond Deep Learning: Charting the Next Frontiers of Affective Computing

Authors :
Andreas Triantafyllopoulos
Lukas Christ
Alexander Gebhard
Xin Jing
Alexander Kathan
Manuel Milling
Iosif Tsangko
Shahin Amiriparian
Björn W. Schuller
Source :
Intelligent Computing, Vol 3 (2024)
Publication Year :
2024
Publisher :
American Association for the Advancement of Science (AAAS), 2024.

Abstract

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.

Details

Language :
English
ISSN :
27715892
Volume :
3
Database :
Directory of Open Access Journals
Journal :
Intelligent Computing
Publication Type :
Academic Journal
Accession number :
edsdoj.9c2bea3e62e4f3d80f7880c826a3920
Document Type :
article
Full Text :
https://doi.org/10.34133/icomputing.0089