Emotion Recognition

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 931–940 of 2041 papers

Title	Date	Tasks	Status	Hype
An Efficient End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition	Sep 20, 2022	Emotion Recognition	—Unverified	0
Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention	Sep 19, 2022	Emotion Recognition	CodeCode Available	1
MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition	Sep 19, 2022	Emotion Recognition	—Unverified	0
Self-Relation Attention and Temporal Awareness for Emotion Recognition via Vocal Burst	Sep 15, 2022	A-VB HighEmotion Recognition	CodeCode Available	0
Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts	Sep 15, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition	Sep 13, 2022	EEGElectroencephalogram (EEG)	—Unverified	0
Automatic Detection of Sentimentality from Facial Expressions	Sep 11, 2022	Emotion Recognition	—Unverified	0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface	Sep 7, 2022	Brain Computer InterfaceEmotion Recognition	—Unverified	0
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning	Sep 2, 2022	Contrastive LearningEEG	—Unverified	0
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents	Aug 30, 2022	Emotion Recognition	—Unverified	0

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All datasets Emomusic RAVDESS EMOTIC FER2013 MaSaC_ERC MPED MSP-Podcast SEED คลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	M2D-CLAP	EmoA	77.4	—	Unverified
2	M2D2	EmoA	76.7	—	Unverified
3	M2D	EmoA	76.1	—	Unverified
4	Jukebox (Pre-training: CALM)	EmoA	72.1	—	Unverified
5	CLMR (Pre-training: contrastive)	EmoA	67.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+Attention	Accuracy	86.7	—	Unverified
2	MultiMAE-DER	WAR	83.61	—	Unverified
3	Intermediate-Attention-Fusion	Accuracy	81.58	—	Unverified
4	Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedST	Accuracy	80.08	—	Unverified
5	ERANN-0-4	Accuracy	74.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CAGE	Top-3 Accuracy (%)	14.73	—	Unverified
2	FocusCLIP	Top-3 Accuracy (%)	13.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VGG based	5-class test accuracy	66.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MaSaC-ERC-Z	F1-score (Weighted)	51.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BiHDM	Accuracy	40.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	w2v2-L-robust-12	Concordance correlation coefficient (CCC)	0.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	4D-aNN	Accuracy	96.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	1'"	1	—	Unverified