学习总结《Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition》
本篇论文被CVPR2019收录 本文亮点: 1. 我们提出了一个A-link推理模型(AIM)来推测可捕获特定动作的潜在关系的actional-links. 这个 actional link 和structural links结合成为广义骨架图. 2. 我们提出了动作-结构图卷积网络(AS-GCN)基于多个图来提取有用的空间和时间信息; 3. 我们引入了一个额外的未来姿态预测头来预测未来的
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- 1. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
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- 3. 论文翻译：Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
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- 5. 译：3D Convolutional Neural Networks for Human Action Recognition
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相关文章
- 1. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 2. Graph Convolutional Networks for Temporal Action Localization
- 3. 论文翻译：Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 4. DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
- 5. 译：3D Convolutional Neural Networks for Human Action Recognition
- 6. 译：Two-stream convolutional networks for action recognition in videos
- 7. 译：Two-Stream Convolutional Networks for Action Recognition in Videos.md
- 8. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition--论文笔记
- 9. 译：Two-Stream Convolutional Networks for Action Recognition in Videos
- 10. 3D Convolutional Neural Networks for Human Action Recognition