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 231–240 of 2041 papers

Title	Date	Tasks	Status	Hype
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities	Aug 1, 2021	Emotion Recognition	CodeCode Available	1
Towards Unbiased Visual Emotion Recognition via Causal Intervention	Jul 26, 2021	Causal InferenceEmotion Recognition	CodeCode Available	1
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity	Jul 18, 2021	EEGElectroencephalogram (EEG)	CodeCode Available	1
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition	Jul 16, 2021	Domain AdaptationEEG	CodeCode Available	1
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation	Jul 14, 2021	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1
Codified audio language modeling learns useful representations for music information retrieval	Jul 12, 2021	Emotion RecognitionGenre classification	CodeCode Available	1
Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion	Jul 2, 2021	Emotion Recognition	CodeCode Available	1
Ethics Sheets for AI Tasks	Jul 2, 2021	ArticlesEmotion Recognition	CodeCode Available	1
iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis	Jul 1, 2021	Emotion Recognition	CodeCode Available	1
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities	Jun 14, 2021	Emotion RecognitionInformation Retrieval	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