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 181–190 of 2041 papers

Title	Date	Tasks	Status	Hype
Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment	Dec 30, 2024	cross-modal alignmentEmotion Recognition	—Unverified	0
A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease	Dec 30, 2024	Emotion RecognitionFacial Emotion Recognition	CodeCode Available	1
Sample Correlation for Fingerprinting Deep Face Recognition	Dec 30, 2024	Adversarial DefenseEmotion Recognition	CodeCode Available	0
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition	Dec 27, 2024	Cross-corpusEmotion Recognition	—Unverified	0
Personalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning	Dec 26, 2024	Emotion RecognitionInformation Retrieval	CodeCode Available	1
A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language	Dec 23, 2024	DiagnosticEmotion Recognition	—Unverified	0
Effective Context Modeling Framework for Emotion Recognition in Conversations	Dec 21, 2024	Emotion Recognition	—Unverified	0
Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition	Dec 19, 2024	Action RecognitionEmotion Recognition	CodeCode Available	1
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition	Dec 18, 2024	Emotion RecognitionMeta-Learning	CodeCode Available	0
Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation	Dec 18, 2024	Domain AdaptationEmotion 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