speech-10

Video Emotion Recognition

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition

Bertram E. Shi, Yuqian Zhou, Didan Deng, Zhaokang Chen•Wed Nov 20 2019

This paper proposes to combine micro- and macro-motion features to improve video emotion recognition with a two-stream recurrent network, named MIMAMO (Micro-Macro-Motion) Net, achieves state of the art performance on two video emotion datasets, the OMG emotion dataset and the Aff-Wild dataset.

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Emotion Recognition in Audio and Video Using Deep Neural Networks

Mandeep Singh, Yuanye Fang•Sun Jun 14 2020

This work attempts to explore different neural networks to improve accuracy of emotion recognition and finds (CNN+RNN) + 3DCNN multi-model architecture which processes audio spectrograms and corresponding video frames giving emotion prediction accuracy of 54.0% among 4 emotions and 71.75% among 3 emotions using IEMOCAP[2] dataset.

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Technical Report for Valence-Arousal Estimation on Affwild2 Dataset

I-Hsuan Li•Mon May 03 2021

This work uses MIMAMO Net \cite{deng2020mimamo} model to achieve information about micro-motion and macro-motion for improving video emotion recognition and achieve Concordance Correlation Coefficient of 0.415 and 0.511 for valence and arousal on the reselected validation set.

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FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference

Qinglan Wei, Xu-Juan Huang•Tue Sep 20 2022

A fully multimodal video-to-emotion system for fast yet effective recognition inference, whose benefits are threefold: the adoption of the hierarchical attention method upon the sound spectra breaks through the limited contribution of the acoustic modality, and outperforms the existing models’ performance on both IEMOCAP and CMU-MOSEI datasets.

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