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 1041–1050 of 2041 papers

Title	Date	Tasks	Status
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions	Jul 1, 2018	Emotion Recognition	—Unverified
Construction of Emotional Lexicon Using Potts Model	Oct 1, 2013	Emotion Recognitionmodel	—Unverified
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas	May 1, 2018	Emotion RecognitionSpeech Recognition	—Unverified
Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition	May 1, 2016	Emotion Recognition	—Unverified
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias	Aug 7, 2018	Emotion Recognition	—Unverified
Context-aware Cascade Attention-based RNN for Video Emotion Recognition	May 30, 2018	DecoderEmotion Classification	—Unverified
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis	Nov 1, 2019	Emotion Recognition	—Unverified
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation	Apr 17, 2024	Emotion ClassificationEmotion Recognition	—Unverified
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition	Oct 28, 2020	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Context-Dependent Models for Predicting and Characterizing Facial Expressiveness	Dec 10, 2019	Emotion 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