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 1081–1090 of 2041 papers

Title	Date	Tasks	Status
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model	Apr 21, 2024	EEGElectroencephalogram (EEG)	—Unverified
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach	Sep 10, 2017	DecoderEmotion Recognition	—Unverified
Robust Emotion Recognition in Context Debiasing	Mar 9, 2024	counterfactualEmotion Recognition	—Unverified
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition	Mar 9, 2022	Emotion RecognitionFederated Learning	—Unverified
Robust Latent Representations via Cross-Modal Translation and Alignment	Nov 3, 2020	Emotion RecognitionTranslation	—Unverified
SAFER: Situation Aware Facial Emotion Recognition	Jun 14, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations	Apr 25, 2024	Emotion-Cause Pair ExtractionEmotion Recognition	—Unverified
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection	Sep 29, 2023	BenchmarkingDiversity	—Unverified
ScalingNet: extracting features from raw EEG data for emotion recognition	Feb 7, 2021	EEGElectroencephalogram (EEG)	—Unverified
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring	Jan 8, 2023	Emotion RecognitionFace 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