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 1601–1625 of 2041 papers

TitleStatusHype
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models—0
Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout—0
Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention—0
Multimodal fusion via cortical network inspired losses—0
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition—0
Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals—0
Multimodal Local-Global Ranking Fusion for Emotion Recognition—0
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition—0
Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition—0
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation—0
Multimodal Relational Tensor Network for Sentiment and Emotion Classification—0
Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis—0
Multi-modal Residual Perceptron Network for Audio-Video Emotion Recognition—0
Multimodal Sentiment Analysis based on Video and Audio Inputs—0
Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition—0
Multimodal Speech Emotion Recognition using Cross Attention with Aligned Audio and Text—0
Multimodal Stress Detection Using Facial Landmarks and Biometric Signals—0
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning—0
Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations—0
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition—0
Multi-Scale Temporal Transformer For Speech Emotion Recognition—0
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition—0
Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation—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