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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Emotion Recognition In Conversation On 7

평가 지표

Accuracy

Weighted F1

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Weighted F1	Paper Title	Repository
MMGCN	79.75	79.72	MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation	-
GraphSmile	86.53	86.52	Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition	-
MM-DFN	80.91	80.83	MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations	-
M3Net	83.67	83.57	Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation
Joyful	-	85.70	Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition	-
DialogueCRN	81.34	81.28	DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations	-
SACL-LSTM	80.70	80.74	Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations	-
COGMEN	-	84.50	COGMEN: COntextualized GNN based Multimodal Emotion recognitioN	-

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Emotion Recognition In Conversation On 7 | SOTA | HyperAI초신경