HyperAI
HomeNewsLatest PapersTutorialsDatasetsWikiSOTALLM ModelsGPU LeaderboardEvents
Search
About
HyperAI

Emotion Recognition In Conversation On 7

Metrics

Accuracy
Weighted F1

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Weighted F1
Paper TitleRepository
MMGCN79.7579.72MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation
GraphSmile86.5386.52Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
MM-DFN80.9180.83MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations
M3Net83.6783.57Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation
Joyful-85.70Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
DialogueCRN81.3481.28DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
SACL-LSTM80.7080.74Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
COGMEN-84.50COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
0 of 8 row(s) selected.