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 161–170 of 2041 papers

Title	Date	Tasks	Status	Hype
Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations	Oct 31, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats	Oct 31, 2022	Emotion Recognition	CodeCode Available	1
GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition	Oct 28, 2022	Emotion RecognitionRepresentation Learning	CodeCode Available	1
Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion	Oct 28, 2022	Emotion RecognitionLanguage Modeling	CodeCode Available	1
Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities	Oct 27, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection	Oct 21, 2022	Dialogue GenerationEmotion Recognition	CodeCode Available	1
Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation	Oct 17, 2022	Contrastive LearningEmotion Recognition	CodeCode Available	1
In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study	Oct 7, 2022	Cross-corpusEmotion Recognition	CodeCode Available	1
Cross Task Neural Architecture Search for EEG Signal Classifications	Oct 1, 2022	Brain Computer InterfaceEEG	CodeCode Available	1
MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations	Oct 1, 2022	Emotion Recognition	CodeCode Available	1

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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