SOTAVerified

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 1951–1975 of 2041 papers

TitleStatusHype
Emotion Knowledge Enhancement for Vision Large Language Models: A Self-Verification Approach for High-Quality Emotion Instruction Data Generation—0
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition—0
Emotion pattern detection on facial videos using functional statistics—0
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models—0
Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities—0
Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning—0
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation—0
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices—0
Emotion recognition by fusing time synchronous and time asynchronous representations—0
Affective Video Content Analysis: Decade Review and New Perspectives—0
Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network—0
Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey—0
Emotion Recognition for In-the-wild Videos—0
Emotion Recognition for Low-Resource Turkish: Fine-Tuning BERTurk on TREMO and Testing on Xenophobic Political Discourse—0
Emotion Recognition for Vietnamese Social Media Text—0
Emotion Recognition From Gait Analyses: Current Research and Future Directions—0
Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN—0
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies—0
Emotion Recognition from Speech based on Relevant Feature and Majority Voting—0
Emotion Recognition from the perspective of Activity Recognition—0
Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis—0
Emotion Recognition in Context—0
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances—0
Conversational Transfer Learning for Emotion Recognition—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4—Unverified
2M2D2EmoA76.7—Unverified
3M2DEmoA76.1—Unverified
4Jukebox (Pre-training: CALM)EmoA72.1—Unverified
5CLMR (Pre-training: contrastive)EmoA67.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7—Unverified
2MultiMAE-DERWAR83.61—Unverified
3Intermediate-Attention-FusionAccuracy81.58—Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08—Unverified
5ERANN-0-4Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73—Unverified
2FocusCLIPTop-3 Accuracy (%)13.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13—Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34—Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64—Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1—Unverified