【論文筆記】CVPR2020 Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
使用GCN進行skeleton-based action recognition Contribution 提出了兩個設計： a disentangled multi-scale aggregation scheme a unified spatial-temporal graph convolutional module (G3D) 分別解決了兩個問題： unbiased weight prob
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相關文章
- 1. Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
- 2. 論文筆記--Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
- 3. Part-based Graph Convolutional Network for Action Recognition論文筆記
- 4. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition--論文筆記
- 5. 【CVPR2020】Temporal Pyramid Network for Action Recognition
- 6. paper reading：Part-based Graph Convolutional Network for Action Recognition
- 7. 論文閱讀《Long-term Temporal Convolutions for Action Recognition》
- 8. PaperReading3-A Closer Look at Spatiotemporal Convolutions for Action Recognition
- 9. 《Long-Term Temporal Convolutions for Action Recognition》 論文翻譯
- 10. 論文閱讀筆記 SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-grained Recognition