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 1931–1940 of 2041 papers

Title	Date	Tasks	Status
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique	Dec 3, 2023	Data AugmentationDecision Making	CodeCode Available
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers	Mar 14, 2024	Activity RecognitionDiversity	CodeCode Available
A Compact Embedding for Facial Expression Similarity	Nov 27, 2018	Action Unit DetectionEmotion Recognition	CodeCode Available
CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations	Jun 10, 2025	Discourse ParsingEmotion Recognition	CodeCode Available
Facial Emotion Recognition: A multi-task approach using deep learning	Oct 28, 2021	Deep LearningEmotion Recognition	CodeCode Available
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis	Apr 17, 2019	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available
ExpNet: Landmark-Free, Deep, 3D Facial Expressions	Feb 2, 2018	3D Face Reconstruction3D Facial Expression Recognition	CodeCode Available
TGCA-PVT: Topic-Guided Context-Aware Pyramid Vision Transformer for Sticker Emotion Recognition	Oct 28, 2024	Emotion Recognition	CodeCode Available
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis	Feb 16, 2023	Emotion ClassificationEmotion Recognition	CodeCode Available
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis	Nov 5, 2020	Emotion Recognitionimage-classification	CodeCode Available

Show:10 25 50

← PrevPage 194 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