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 771–780 of 2041 papers

Title	Date	Tasks	Status	Hype
Versatile audio-visual learning for emotion recognition	May 12, 2023	Arousal EstimationAttribute	—Unverified	0
Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network	May 11, 2023	Emotion Recognition	—Unverified	0
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications	May 9, 2023	Emotion Recognitionintent-classification	—Unverified	0
Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive Mixup	May 7, 2023	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	0
CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition	May 7, 2023	EEGEEG Emotion Recognition	—Unverified	0
A multimodal dynamical variational autoencoder for audiovisual speech representation learning	May 5, 2023	DenoisingDisentanglement	CodeCode Available	0
High-Level Context Representation for Emotion Recognition in Images	May 5, 2023	Emotion RecognitionVocal Bursts Intensity Prediction	—Unverified	0
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation	May 5, 2023	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available	1
A vector quantized masked autoencoder for audiovisual speech emotion recognition	May 5, 2023	Contrastive LearningEmotion Recognition	—Unverified	0
Noise-Resistant Multimodal Transformer for Emotion Recognition	May 4, 2023	Emotion RecognitionMultimodal 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