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Emotion Recognition In Conversation On 7

評価指標

Accuracy

Weighted F1

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

			Paper Title
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

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Emotion Recognition In Conversation On 7 | SOTA | HyperAI超神経