[NLU] DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
總述： 最大的特色就是將GCN用在了對話情感識別上，這裏的coversation可能是不止兩個人的。然後故事寫得很好，inter-dependency和intra-dependency還有self-dependency唬的我一愣一愣的。 主要模型： inter-depencency intra-dependency 所謂的inter-dependency其實就是說某一個speak對其他spea
相關文章
- 1. Emotion Recognition Using Graph Convolutional Networks
- 2. DAILY PAPER 003 -- DialogueGCN: A Graph Convolutional Neural Network for ERC
- 3. SemEval2019Task3_ERC | (6) Hybrid Features for Emotion Recognition in Textual Conversation
- 4. DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
- 5. Paper：Extended deep neural network for facial emotion recognition（2019)
- 6. Recurrent Neural Networks for Emotion Recognition in Video
- 7. paper reading：Part-based Graph Convolutional Network for Action Recognition
- 8. Convolutional Neural Network-based Place Recognition
- 9. Tensorflow - Implement for a Convolutional Neural Network on MNIST.
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相關文章
- 1. Emotion Recognition Using Graph Convolutional Networks
- 2. DAILY PAPER 003 -- DialogueGCN: A Graph Convolutional Neural Network for ERC
- 3. SemEval2019Task3_ERC | (6) Hybrid Features for Emotion Recognition in Textual Conversation
- 4. DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
- 5. Paper：Extended deep neural network for facial emotion recognition（2019)
- 6. Recurrent Neural Networks for Emotion Recognition in Video
- 7. paper reading：Part-based Graph Convolutional Network for Action Recognition
- 8. Convolutional Neural Network-based Place Recognition
- 9. Tensorflow - Implement for a Convolutional Neural Network on MNIST.
- 10. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation