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 951–960 of 2041 papers

Title	Date	Tasks	Status
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding	Jun 29, 2018	Action RecognitionEmotion Recognition	—Unverified
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation	Sep 7, 2021	Emotion RecognitionFew-Shot Learning	—Unverified
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition	Sep 6, 2020	Emotion Recognitionfeature selection	—Unverified
FindingEmo: An Image Dataset for Emotion Recognition in the Wild	Feb 2, 2024	Emotion Recognition	—Unverified
Finding Good Representations of Emotions for Text Classification	Aug 22, 2018	Abusive LanguageClassification	—Unverified
Findings of the Shared Task on Emotion Analysis in Tamil	May 1, 2022	Emotion Recognition	—Unverified
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model	Jun 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Fine-Grained Emotion Detection in Health-Related Online Posts	Oct 1, 2018	Emotion Recognition	—Unverified
Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation	Jun 1, 2013	Emotion ClassificationEmotion Recognition	—Unverified
Affective Video Content Analysis: Decade Review and New Perspectives	Oct 26, 2023	Emotional IntelligenceEmotion Recognition	—Unverified

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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