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 1331–1340 of 2041 papers

Title	Date	Tasks	Status	Hype
Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning	Mar 30, 2021	Emotion RecognitionSelf-Learning	—Unverified	0
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments	Mar 30, 2021	Emotion RecognitionFederated Learning	—Unverified	0
Pre-training strategies and datasets for facial representation learning	Mar 30, 2021	3D Face Reconstruction3D Facial Landmark Localization	CodeCode Available	1
Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition	Mar 25, 2021	Emotion RecognitionFacial Emotion Recognition	CodeCode Available	0
Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition	Mar 24, 2021	Arousal EstimationEmotion Recognition	—Unverified	0
Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer	Mar 23, 2021	Emotion Recognition	—Unverified	0
Responsible AI: Gender bias assessment in emotion recognition	Mar 21, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified	0
Computational Emotion Analysis From Images: Recent Advances and Future Directions	Mar 19, 2021	Domain AdaptationEmotion Recognition	—Unverified	0
Multimodal End-to-End Sparse Model for Emotion Recognition	Mar 17, 2021	Emotion Recognition	CodeCode Available	1
Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition	Mar 16, 2021	Deep LearningEmotion 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