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
Analyzing the Influence of Dataset Composition for Emotion Recognition—0
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild—0
An Empirical Study and Improvement for Speech Emotion Recognition—0
A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion—0
A Robust Incomplete Multimodal Low-Rank Adaptation Approach for Emotion Recognition—0
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
A Unified Transformer-based Network for multimodal Emotion Recognition—0
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation—0
Bias and Fairness on Multimodal Emotion Detection Algorithms—0
CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation—0
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition—0
Context-aware Cascade Attention-based RNN for Video Emotion Recognition—0
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition—0
Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition—0
Continuous Multimodal Emotion Recognition Approach for AVEC 2017—0
Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition—0
Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data—0
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition—0
cross-modal fusion techniques for utterance-level emotion recognition from text and speech—0
CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition—0
Deep CNN with late fusion for realtime multimodal emotion recognition—0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations—0
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition—0
Do Multimodal Emotion Recognition Models Tackle Ambiguity?—0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation—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