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 671–680 of 2041 papers

Title	Date	Tasks	Status
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition	Aug 17, 2024	Contrastive LearningCross-corpus	—Unverified
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters	Oct 19, 2021	EEGEeg Decoding	—Unverified
EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in \#Tweets	Jun 1, 2018	BIG-bench Machine LearningEmotion Recognition	—Unverified
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue	Oct 1, 2020	Emotion RecognitionOpinion Mining	—Unverified
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification	Jul 20, 2024	EEGElectroencephalogram (EEG)	—Unverified
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition	May 26, 2025	DiversityEmotion Recognition	—Unverified
EmoNets: Multimodal deep learning approaches for emotion recognition in video	Mar 5, 2015	Deep LearningEmotion Recognition	—Unverified
Biologically inspired speech emotion recognition	Nov 15, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition	Dec 28, 2023	Contrastive LearningEmotion Recognition	—Unverified
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition	Feb 7, 2021	EEGElectroencephalogram (EEG)	—Unverified

Show:10 25 50

← PrevPage 68 of 205Next →

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