DeepVANet: A Deep End-to-End Network for Multi-modal Emotion Recognition

2021 ◽  
pp. 227-237
Author(s):  
Yuhao Zhang ◽  
Md Zakir Hossain ◽  
Shafin Rahman
Keyword(s):  
Emotion Recognition ◽  
End To End

scholarly journals Multimodal End-to-End Sparse Model for Emotion Recognition

2021 ◽  
Author(s):  
Wenliang Dai ◽  
Samuel Cahyawijaya ◽  
Zihan Liu ◽  
Pascale Fung
Keyword(s):  
Emotion Recognition ◽  
Sparse Model ◽  
End To End

scholarly journals An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

2020 ◽  
Vol 34 (01) ◽  
pp. 303-311 ◽  
Author(s):  
Sicheng Zhao ◽  
Yunsheng Ma ◽  
Yang Gu ◽  
Jufeng Yang ◽  
Tengfei Xing ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
State Of The Art ◽  
Source Code ◽  
Cross Entropy ◽  
Attention Network ◽  
Audio Features ◽  
End To End ◽  
3D Cnn ◽  
And Training

Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extracting visual and/or audio features and training classifiers. In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classification loss, i.e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https://github.com/maysonma/VAANet.

scholarly journals End-to-end learning for dimensional emotion recognition from physiological signals

2017 ◽  
Author(s):  
Gil Keren ◽  
Tobias Kirschstein ◽  
Erik Marchi ◽  
Fabien Ringeval ◽  
Bjorn Schuller
Keyword(s):  
Emotion Recognition ◽  
Physiological Signals ◽  
End To End

scholarly journals End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

2017 ◽  
Vol 11 (8) ◽  
pp. 1301-1309 ◽  
Author(s):  
Panagiotis Tzirakis ◽  
George Trigeorgis ◽  
Mihalis A. Nicolaou ◽  
Bjorn W. Schuller ◽  
Stefanos Zafeiriou
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks ◽  
End To End ◽  
Multimodal Emotion Recognition
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