SOTAVerified

Multimodal Emotion Recognition

This is a leaderboard for multimodal emotion recognition on the IEMOCAP dataset. The modality abbreviations are A: Acoustic T: Text V: Visual

Please include the modality in the bracket after the model name.

All models must use standard five emotion categories and are evaluated in standard leave-one-session-out (LOSO). See the papers for references.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 180 papers

TitleStatusHype
Multimodal Sentiment Analysis using Hierarchical Fusion with Context ModelingCode0
Multimodal Speech Emotion Recognition and Ambiguity ResolutionCode0
Multimodal Speech Emotion Recognition Using Audio and TextCode0
Multi Teacher Privileged Knowledge Distillation for Multimodal Expression RecognitionCode0
Learning Alignment for Multimodal Emotion Recognition from SpeechCode0
Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout—0
Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention—0
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition—0
MVP: Multimodal Emotion Recognition based on Video and Physiological Signals—0
Noise-Resistant Multimodal Transformer for Emotion Recognition—0
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences—0
PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System—0
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition—0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum—0
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring—0
Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors—0
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features—0
TACOformer:Token-channel compounded Cross Attention for Multimodal Emotion Recognition—0
Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning—0
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause—0
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion—0
Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition—0
Using Large Pre-Trained Models with Cross-Modal Attention for Multi-Modal Emotion Recognition—0
Versatile audio-visual learning for emotion recognition—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
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All datasetsIEMOCAP-4MELDIEMOCAPCMU-MOSEI-SentimentCMU-MOSEI-Sentiment-3Expressive hands and faces dataset (EHF).MELD-Sentiment

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GraphSmileWeighted F186.52—Unverified
2JoyfulWeighted F185.7—Unverified
3COGMENWeighted F184.5—Unverified
4DANNAccuracy82.7—Unverified
5MMERAccuracy81.7—Unverified
6PATHOSnet v2Accuracy80.4—Unverified
7Self-attention weight correction (A+T)Accuracy76.8—Unverified
8CHFusionAccuracy76.5—Unverified
9bc-LSTMWeighted F174.1—Unverified
10Audio + Text (Stage III)F170.5—Unverified
#ModelMetricClaimedVerifiedStatus
1GraphSmileWeighted F166.71—Unverified
2Audio + Text (Stage III)Weighted F165.8—Unverified
3JoyfulWeighted F161.77—Unverified
#ModelMetricClaimedVerifiedStatus
1GraphSmileWeighted F172.81—Unverified
2JoyfulWeighted F170.5—Unverified
#ModelMetricClaimedVerifiedStatus
1GraphSmileWeighted F144.93—Unverified
#ModelMetricClaimedVerifiedStatus
1GraphSmileWeighted F166.73—Unverified
#ModelMetricClaimedVerifiedStatus
1SMPLify-Xv2v error52.9—Unverified
#ModelMetricClaimedVerifiedStatus
1GraphSmileWeighted F174.31—Unverified