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 921–930 of 2041 papers

Title	Date	Tasks	Status
Facial Emotion Recognition Using Deep Learning	Oct 19, 2019	Deep LearningEmotion Recognition	—Unverified
Facial Emotion Recognition using Deep Residual Networks in Real-World Environments	Nov 4, 2021	Emotion RecognitionFacial Emotion Recognition	—Unverified
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation	Dec 4, 2024	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Adaptive Fusion Techniques for Multimodal Data	Nov 10, 2019	Emotion RecognitionMachine Translation	—Unverified
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems	Apr 18, 2021	Adversarial AttackAutomatic Speech Recognition	—Unverified
Facial Emotions Recognition using Convolutional Neural Net	Jan 6, 2020	Emotion RecognitionFace Detection	—Unverified
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets	Jul 23, 2019	Data AugmentationEmotion Recognition	—Unverified
Facial Expression Recognition and Image Description Generation in Vietnamese	Aug 12, 2022	DescriptiveEmotion Recognition	—Unverified
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection	Sep 13, 2023	DisentanglementEmotion Recognition	—Unverified
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation	Jan 17, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified

Show:10 25 50

← PrevPage 93 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