Beyond Deep Learning: Charting the Next Frontiers of Affective Computing

MCML Authors

Andreas Triantafyllopoulos

→ Group Björn Schuller
 Health Informatics

Alexander Gebhard

→ Group Björn Schuller
 Health Informatics

Xin Jing

→ Group Björn Schuller
 Health Informatics

Alexander Kathan

→ Group Björn Schuller
 Health Informatics

Manuel Milling

→ Group Björn Schuller
 Health Informatics

Iosif Tsangko

→ Group Björn Schuller
 Health Informatics

Shahin Amiriparian

Dr.

→ Group Björn Schuller
 Health Informatics

Björn Schuller

Prof. Dr.

Principal Investigator

Health Informatics

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.

article

Intelligent Computing

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Research Area

 B3 | Multimodal Perception