Emotion Recognition In Conversation On Cmu 2

Metrics

Accuracy

Weighted F1

Results

Performance results of various models on this benchmark

			Paper Title
GraphSmile	46.82	44.93	Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
MMGCN	45.67	44.11	MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation
COGMEN	-	43.90	COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
MM-DFN	45.29	42.98	MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations
M3Net	43.67	41.12	Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation
DialogueCRN	37.88	26.55	DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
SACL-LSTM	38.60	25.95	Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations

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Emotion Recognition In Conversation On Cmu 2 | SOTA | HyperAI