Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition--論文筆記
論文題目《基於雙流結構的自適應圖卷積網絡的骨骼姿態識別》引言 1.引言 作者提出了雙流自適應圖卷積（2s-AGCN），在兩個2個數據集上進行了大量的實驗，都實現了最先進的性能。 貢獻 1.提出了自適應的圖卷積網絡結構針對不同的圖卷積層和端到端的骨骼樣本能夠更好的學習其拓撲結構，也能夠更好的適用於識別任務和其層次結構。 2.二階信息通過雙流結構和一階信息聯合起來，對提高性能起到了很好地作用 3.在兩
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相關文章
- 1. 論文翻譯：Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
- 2. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 3. 論文：3D Convolutional Neural Networks for Human Action Recognition
- 4. 論文瀏覽(32) Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 5. 論文翻譯：Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 6. Part-based Graph Convolutional Network for Action Recognition論文筆記
- 7. 論文筆記3D Convolutional Neural Networks for Human Action Recognition
- 8. 《Two-Stream Convolutional Networks for Action Recognition in Videos》論文筆記
- 9. 《3D Convolutional Neural Networks for Human Action Recognition》論文閱讀筆記
- 10. Graph Convolutional Networks for Temporal Action Localization