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 471–480 of 2041 papers

Title	Date	Tasks	Status
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling	May 18, 2022	Action Unit DetectionEmotion Recognition	—Unverified
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition	Dec 2, 2024	Active LearningCross-corpus	—Unverified
Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition	Jun 20, 2024	Emotion RecognitionKnowledge Distillation	—Unverified
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition	Jul 6, 2023	EEGEEG Emotion Recognition	—Unverified
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English	May 31, 2019	Emotion ClassificationEmotion Recognition	—Unverified
Crowdsourcing a Word-Emotion Association Lexicon	Aug 28, 2013	Emotion Recognition	—Unverified
Crowdsourcing-based Annotation of Emotions in Filipino and English Tweets	Dec 1, 2016	Emotion Recognition	—Unverified
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons	Jun 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal	May 31, 2022	Emotion Recognition	—Unverified
Computationally Efficient Wasserstein Loss for Structured Labels	Mar 1, 2021	Age EstimationEmotion 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