Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
綜述 3D人體動作識別的現代深度模型的鑑別力越來越強。隨着最近3D人體骨骼動作再現技術的復興，3D人體骨骼動作再現技術的質量和發展速度都有了長足的進步。然而，基於最先進的學習方法的內部工作原理在三維人體動作識別中仍然主要是黑盒子。在這項工作中，我們提出使用一種新的模型稱爲時間卷積神經網絡(TCN)用於三維人體動作識別。與目前流行的基於lstm的遞歸神經網絡模型相比，TCN爲我們提供了一
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相關文章
- 1. 論文：3D Convolutional Neural Networks for Human Action Recognition
- 2. 3DCNN:3D Convolutional Neural Networks for Human Action Recognition
- 3. 3D Convolutional Neural Networks for Human Action Recognition
- 4. 譯：3D Convolutional Neural Networks for Human Action Recognition
- 5. Graph Convolutional Networks for Temporal Action Localization
- 6. TCN: TEMPORAL CONVOLUTIONAL NETWORKS
- 7. 讀書筆記9：Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
- 8. 論文閱讀筆記（三十九）：3D Convolutional Neural Networks for Human Action Recognition
- 9. Reading Note: Interpretable Convolutional Neural Networks
- 10. P-GCN：Graph Convolutional Networks for Temporal Action Localization 2019 ICCV