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 1181–1190 of 2041 papers

Title	Date	Tasks	Status	Hype
TNTC: two-stream network with transformer-based complementarity for gait-based emotion recognition	Oct 26, 2021	Emotion Recognition	—Unverified	0
Emotion recognition in talking-face videos using persistent entropy and neural networks	Oct 26, 2021	Emotion Recognition	CodeCode Available	0
SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network	Oct 24, 2021	Emotion RecognitionObject	—Unverified	0
SSAST: Self-Supervised Audio Spectrogram Transformer	Oct 19, 2021	Audio ClassificationClassification	CodeCode Available	2
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters	Oct 19, 2021	EEGEeg Decoding	—Unverified	0
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation	Oct 16, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
Hybrid Mutimodal Fusion for Dimensional Emotion Recognition	Oct 16, 2021	Emotion Recognition	—Unverified	0
Span Detection for Aspect-Based Sentiment Analysis in Vietnamese	Oct 15, 2021	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Multimodal Emotion-Cause Pair Extraction in Conversations	Oct 15, 2021	ArticlesEmotion Cause Extraction	—Unverified	0
Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition	Oct 13, 2021	EEGEEG Emotion Recognition	—Unverified	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