Home Console Docs News Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

Emotion Recognition In Conversation On 7

Metrics

Accuracy

Weighted F1

Results

Performance results of various models on this benchmark

			Paper Title	Repository
GraphSmile	86.53	86.52	Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
Joyful	-	85.70	Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
COGMEN	-	84.50	COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
M3Net	83.67	83.57	Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation	-
DialogueCRN	81.34	81.28	DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
MM-DFN	80.91	80.83	MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations
SACL-LSTM	80.70	80.74	Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
MMGCN	79.75	79.72	MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation

0 of 8 row(s) selected.

Emotion Recognition In Conversation On 7 | SOTA | HyperAI