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 501–510 of 2041 papers

Title	Date	Tasks	Status	Hype
Joint Multimodal Transformer for Emotion Recognition in the Wild	Mar 15, 2024	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers	Mar 14, 2024	Activity RecognitionDiversity	CodeCode Available	0
A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product	Mar 13, 2024	Emotion Recognitionobject-detection	—Unverified	0
CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation	Mar 12, 2024	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
Group Movie Selection using Multi-channel Emotion Recognition	Mar 11, 2024	Decision MakingEmotion Recognition	—Unverified	0
Human Pose Descriptions and Subject-Focused Attention for Improved Zero-Shot Transfer in Human-Centric Classification Tasks	Mar 11, 2024	Activity RecognitionAge Classification	—Unverified	0
Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey	Mar 9, 2024	Emotion Recognition	—Unverified	0
Robust Emotion Recognition in Context Debiasing	Mar 9, 2024	counterfactualEmotion Recognition	—Unverified	0
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing	Mar 9, 2024	Emotion RecognitionFacial Action Unit Detection	CodeCode Available	1
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention Mechanism	Mar 7, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1

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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