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 1521–1530 of 2041 papers

Title	Date	Tasks	Status
The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems	Jul 29, 2020	Emotion RecognitionFacial Emotion Recognition	—Unverified
Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge	Jul 28, 2020	ClassificationEmotion Recognition	—Unverified
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text	Jul 28, 2020	ArticlesDeep Learning	—Unverified
DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis	Jul 28, 2020	ClassificationEmotion Recognition	CodeCode Available
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis	Jul 28, 2020	Emotion RecognitionSentiment Analysis	CodeCode Available
HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild	Jul 24, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition	Jul 19, 2020	Emotion RecognitionFacial Emotion Recognition	—Unverified
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning	Jul 14, 2020	Disease PredictionEmotion Recognition	—Unverified
COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes	Jul 14, 2020	DescriptiveEmotion Recognition	CodeCode Available
Temporal aggregation of audio-visual modalities for emotion recognition	Jul 8, 2020	Emotion 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