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 em...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Intelligent computing 2024-01, Vol.3
Hauptverfasser: Triantafyllopoulos, Andreas, Christ, Lukas, Gebhard, Alexander, Jing, Xin, Kathan, Alexander, Milling, Manuel, Tsangko, Iosif, Amiriparian, Shahin, Schuller, Björn W.
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:2771-5892
2771-5892
DOI:10.34133/icomputing.0089