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 511–520 of 2041 papers

Title	Date	Tasks	Status	Score
An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild	Jul 7, 2021	Emotion Recognition	CodeCode Available	5
End-to-End Multimodal Emotion Recognition using Deep Neural Networks	Apr 27, 2017	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	5
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation	Nov 19, 2024	Domain AdaptationEEG	CodeCode Available	5
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks	Oct 7, 2021	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning	Sep 30, 2022	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation	Feb 20, 2022	Domain AdaptationEEG	CodeCode Available	5
Exploiting Multiple EEG Data Domains with Adversarial Learning	Apr 16, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	5
EmotionX-KU: BERT-Max based Contextual Emotion Classifier	Jun 27, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available	5
An Attention Model for group-level emotion recognition	Jul 9, 2018	Emotion Recognitionmodel	CodeCode Available	5
EmoTxt: A Toolkit for Emotion Recognition from Text	Aug 13, 2017	Emotion ClassificationEmotion Recognition	CodeCode Available	5

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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