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 1291–1300 of 2041 papers

Title	Date	Tasks	Status
Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition	Oct 13, 2021	EEGElectroencephalogram (EEG)	—Unverified
Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition	Oct 13, 2021	EEGEEG Emotion Recognition	—Unverified
Real-time EEG-based Emotion Recognition using Discrete Wavelet Transforms on Full and Reduced Channel Signals	Oct 11, 2021	EEGElectroencephalogram (EEG)	—Unverified
Cross Domain Emotion Recognition using Few Shot Knowledge Transfer	Oct 11, 2021	Emotion RecognitionTransfer Learning	—Unverified
Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective	Oct 8, 2021	Emotion Recognition	—Unverified
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition	Oct 8, 2021	Emotion RecognitionObject	—Unverified
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks	Oct 7, 2021	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Unsupervised Multimodal Language Representations using Convolutional Autoencoders	Oct 6, 2021	Emotion RecognitionMultimodal Sentiment Analysis	—Unverified
Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews	Oct 1, 2021	Emotion RecognitionSentiment Analysis	—Unverified
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics	Oct 1, 2021	ClassificationEmotion Classification	—Unverified

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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