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 141–150 of 2041 papers

Title	Date	Tasks	Status	Hype
Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities	Feb 2, 2025	Emotion Recognition	—Unverified	0
SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition	Feb 1, 2025	DenoisingEmotion Recognition	CodeCode Available	1
Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition	Feb 1, 2025	EEGElectroencephalogram (EEG)	CodeCode Available	1
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design	Jan 30, 2025	Emotion RecognitionFacial Emotion Recognition	—Unverified	0
Divergent Emotional Patterns in Disinformation on Social Media? An Analysis of Tweets and TikToks about the DANA in Valencia	Jan 28, 2025	Emotion Recognition	—Unverified	0
Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study	Jan 28, 2025	Emotion RecognitionInformation Retrieval	—Unverified	0
Multimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence	Jan 28, 2025	Depression DetectionEmotion Recognition	—Unverified	0
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition	Jan 26, 2025	Domain AdaptationEmotion Recognition	—Unverified	0
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition	Jan 25, 2025	cross-modal alignmentEmotion Classification	—Unverified	0
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding	Jan 25, 2025	Action UnderstandingEmotion Recognition	—Unverified	0

Show:10 25 50

← PrevPage 15 of 205Next →

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