學習總結《Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition》
本篇論文被CVPR2019收錄 本文亮點: 1. 我們提出了一個A-link推理模型(AIM)來推測可捕獲特定動作的潛在關係的actional-links. 這個 actional link 和structural links結合成爲廣義骨架圖. 2. 我們提出了動作-結構圖卷積網絡(AS-GCN)基於多個圖來提取有用的空間和時間信息; 3. 我們引入了一個額外的未來姿態預測頭來預測未來的
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- 8. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition--論文筆記
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相關文章
- 1. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 2. Graph Convolutional Networks for Temporal Action Localization
- 3. 論文翻譯：Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 4. DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
- 5. 譯：3D Convolutional Neural Networks for Human Action Recognition
- 6. 譯：Two-stream convolutional networks for action recognition in videos
- 7. 譯：Two-Stream Convolutional Networks for Action Recognition in Videos.md
- 8. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition--論文筆記
- 9. 譯：Two-Stream Convolutional Networks for Action Recognition in Videos
- 10. 3D Convolutional Neural Networks for Human Action Recognition