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

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Showing 1651–1660 of 2041 papers

Title	Date	Tasks	Status
EEG based Emotion Recognition of Image Stimuli	Sep 11, 2019	EEGElectroencephalogram (EEG)	—Unverified
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression	Sep 11, 2019	Deep AttentionEmotion Classification	CodeCode Available
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography	Sep 11, 2019	EEGElectroencephalogram (EEG)	CodeCode Available
Multimodal Embeddings from Language Models	Sep 10, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available
Learning Alignment for Multimodal Emotion Recognition from Speech	Sep 6, 2019	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available
Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning	Sep 6, 2019	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Sentiment and Emotion Based Representations for Fake Reviews Detection	Sep 1, 2019	Emotion Recognition	—Unverified
Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods	Aug 28, 2019	Emotion Recognitionfeature selection	—Unverified
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning	Aug 23, 2019	ClassificationEmotion Classification	—Unverified
EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation	Aug 17, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available

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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