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 31–40 of 2041 papers

Title	Date	Tasks	Status	Hype
Personalized Large Language Models	Feb 14, 2024	Emotion RecognitionHate Speech Detection	CodeCode Available	2
HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition	Jan 11, 2024	Contrastive LearningDynamic Facial Expression Recognition	CodeCode Available	2
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT	Oct 7, 2023	Audio captioningAutomatic Speech Recognition	CodeCode Available	2
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning	Apr 18, 2023	Emotion RecognitionMulti-Label Learning	CodeCode Available	2
Towards Interpretable Mental Health Analysis with Large Language Models	Apr 6, 2023	Causal Emotion EntailmentEmotion Recognition	CodeCode Available	2
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition	Nov 21, 2022	Contrastive LearningEmotion Recognition	CodeCode Available	2
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI	May 29, 2022	Chinese Sentiment AnalysisConversational Response Generation	CodeCode Available	2
EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis	May 4, 2022	Emotion Recognition	CodeCode Available	2
EMOCA: Emotion Driven Monocular Face Capture and Animation	Apr 24, 2022	3D Face Reconstruction3D geometry	CodeCode Available	2
Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices	Mar 25, 2022	Arousal EstimationEmotion 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