論文翻譯：Skeleton-Based Action Recognition with Directed Graph Neural Networks
骨架數據由於能夠較好地適應動態環境和複雜背景，在動作識別任務中得到了廣泛的應用。在現有的方法中，骨骼數據中的關節和骨骼信息都被證明對動作識別任務有很大的幫助。然而，如何將這兩種類型的數據結合在一起，最大限度地利用關節和骨骼之間的關係，仍然是一個有待解決的問題。在這項工作中，我們將骨骼數據表示爲基於關節和骨骼之間的運動學依賴i的有向無環圖(DAG)設計了一種新型有向圖神經網絡，用於提取關節
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相關文章
- 1. Skeleton-Based Action Recognition with Directed Graph Neural Networks---論文筆記
- 2. 論文翻譯：Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
- 3. 論文翻譯：Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 4. Action Recognition Using Attention-Joints Graph Convolutional Neural Networks翻譯
- 5. 論文翻譯：Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
- 6. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS翻譯
- 7. 論文：3D Convolutional Neural Networks for Human Action Recognition
- 8. 論文翻譯：GRAPH ATTENTION NETWORKS
- 9. DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
- 10. 論文翻譯:View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skel