P-GCN：Graph Convolutional Networks for Temporal Action Localization 2019 ICCV
论文下载链接：https://arxiv.org/pdf/1911.11462.pdf 1 摘要 大多数最先进的行为定位系统都是单独处理每个动作proposal，而不是在学习过程中显式地利用它们之间的关系。然而，proposal之间的关系实际上在行动定位中扮演着重要的角色，因为一个有意义的行动总是由一个视频中的多个proposal组成。在本文中，我们提出利用图卷积网络(GCNs)来挖掘propos
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相关文章
- 1. Graph Convolutional Networks for Temporal Action Localization
- 2. [行为检测] CDC-Convolutional-De-Convolutional Networks for Precise Temporal Action Localization
- 3. [行为检测] CDC-Convolutional-De-Convolutional Networks for Precise Temporal Action Location
- 4. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 5. G-TAD: Sub-Graph Localization for Temporal Action Detection
- 6. DenseCap：Fully Convolutional Localization Networks for Dense Captioning
- 7. TCN: TEMPORAL CONVOLUTIONAL NETWORKS
- 8. Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
- 9. MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
- 10. 译：Two-stream convolutional networks for action recognition in videos