DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
Introduction 作者認爲解決如下兩個問題能有效增強GCN在動作識別中的能力： 1.在人類骨骼的不同部位中有着時空關聯性，但這些關聯性是動態的，而且在時空域中不同的動作關聯性也是不同的。因此提取這些關聯性很困難，標椎卷積操作普遍採納的傳統GCN是靜態的，而且僅僅描述了鄰居節點的空間聯繫，因而不能準確的獲得這樣的動態時空聯繫。 2.骨骼的空間層次結構和運動的時間序列特性都編碼了順序信息，這在
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相關文章
- 1. paper reading：Part-based Graph Convolutional Network for Action Recognition
- 2. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
- 3. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 4. 【論文翻譯】An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
- 5. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
- 6. Part-based Graph Convolutional Network for Action Recognition論文筆記
- 7. 譯：Convolutional Two-Stream Network Fusion for Video Action Recognition
- 8. [NLU] DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
- 9. Emotion Recognition Using Graph Convolutional Networks
- 10. Graph Convolutional Networks for Temporal Action Localization