Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition-Reference-Cited by-同舟云学术

Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition

Published:2022-06 Issue:2 Volume:14 Page:715-729
ISSN:2379-8920
Container-title:IEEE Transactions on Cognitive and Developmental Systems
language:
Short-container-title:IEEE Trans. Cogn. Dev. Syst.

Author:

Liu Wei¹^ORCID,Qiu Jie-Lin²,Zheng Wei-Long³^ORCID,Lu Bao-Liang¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Brain Science and Technology Research Center, Center for Brain-Like Computing and Machine Intelligence, Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China

2. Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA

3. Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

SJTU Transmed Awards Research

Fundamental Research Funds for the Central Universities

Higher Education Discipline Innovation Project

China Southern Power Grid

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject