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 451–460 of 2041 papers

Title	Date	Tasks	Status	Hype
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model	Apr 21, 2024	EEGElectroencephalogram (EEG)	—Unverified	0
MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention	Apr 21, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems	Apr 21, 2024	Emotion RecognitionMovie Recommendation	—Unverified	0
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News	Apr 21, 2024	BenchmarkingEmotion Recognition	CodeCode Available	0
Cooperative Sentiment Agents for Multimodal Sentiment Analysis	Apr 19, 2024	DisentanglementEmotion Recognition	CodeCode Available	1
TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition	Apr 19, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities	Apr 18, 2024	Emotion RecognitionMultimodal Emotion Recognition	—Unverified	0
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models	Apr 18, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified	0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation	Apr 17, 2024	Emotion ClassificationEmotion Recognition	—Unverified	0
Deep CNN with late fusion for realtime multimodal emotion recognition	Apr 15, 2024	Emotion RecognitionMultimodal Emotion 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