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 21–30 of 2041 papers

Title	Date	Tasks	Status	Hype
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective	Sep 11, 2024	Aspect-Based Sentiment AnalysisEmotion Recognition	CodeCode Available	2
MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning	Jul 8, 2024	Arousal EstimationEEG	CodeCode Available	2
Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition	Jul 7, 2024	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available	2
EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition	Jun 26, 2024	EEGEEG Emotion Recognition	CodeCode Available	2
Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion Recognition	Jun 20, 2024	DiagnosticEEG	CodeCode Available	2
Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges	Jun 13, 2024	Emotion RecognitionMusic Emotion Recognition	CodeCode Available	2
BLSP-Emo: Towards Empathetic Large Speech-Language Models	Jun 6, 2024	Emotion RecognitionInstruction Following	CodeCode Available	2
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild	Apr 13, 2024	cross-modal alignmentDynamic Facial Expression Recognition	CodeCode Available	2
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition	Feb 20, 2024	Emotion RecognitionSelf-Supervised Learning	CodeCode Available	2
EmoBench: Evaluating the Emotional Intelligence of Large Language Models	Feb 19, 2024	Emotional IntelligenceEmotion Recognition	CodeCode Available	2

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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