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

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Showing 461–470 of 2041 papers

Title	Date	Tasks	Status	Hype
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition	Apr 15, 2024	Contrastive LearningCross-corpus	—Unverified	0
Customising General Large Language Models for Specialised Emotion Recognition Tasks	Apr 14, 2024	Emotion RecognitionLanguage Modeling	CodeCode Available	0
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild	Apr 13, 2024	cross-modal alignmentDynamic Facial Expression Recognition	CodeCode Available	2
Improving Personalisation in Valence and Arousal Prediction using Data Augmentation	Apr 13, 2024	Data AugmentationEmotion Recognition	—Unverified	0
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations	Apr 12, 2024	Emotion RecognitionRepresentation Learning	—Unverified	0
The Power of Properties: Uncovering the Influential Factors in Emotion Classification	Apr 11, 2024	Decision MakingEmotion Classification	—Unverified	0
Resolve Domain Conflicts for Generalizable Remote Physiological Measurement	Apr 11, 2024	AttributeEmotion Recognition	CodeCode Available	1
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios	Apr 11, 2024	Emotion RecognitionLanguage Modelling	—Unverified	0
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions	Apr 10, 2024	Emotion RecognitionKeyword Spotting	CodeCode Available	0
nEMO: Dataset of Emotional Speech in Polish	Apr 9, 2024	Audio ClassificationEmotion Recognition	CodeCode Available	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