論文筆記Learning object-action relations from bimanual human demonstration using graph networks
摘要 提供了雙手演示的動作分類框架。從原始RGB-D視頻提取物體幾何動作關係，建立基於圖的分類器。 圖1：數據集：來自數據集的3個示例性RGB-D圖像（爲了更好的清晰度而編輯的圖像）；特徵提取：三級處理流水線的中間結果和最終結果，一個場景圖；分類：基於場景圖對右手（RH）和左手（LH）執行的動作進行預測的圖形網絡分類器。 圖2：從我們提議的數據集的示範性記錄。第一排：準備早餐麥片，切一根香蕉，倒進
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相關文章
- 1. 【論文筆記】Learning Structural Similarity of User Interface Layouts using Graph Networks
- 2. 【論文筆記】Graph Transformer Networks
- 3. 論文筆記之Learning Human-Object Interaction Detection using Interaction Points
- 4. GRAPH ATTENTION NETWORKS論文筆記
- 5. 【論文筆記】Graph Matching Networks for Learning the Similarity of Graph Structured Objects
- 6. Emotion Recognition Using Graph Convolutional Networks
- 7. 論文閱讀：Learning Human-Object Interactions by Graph Parsing Neural Networks
- 8. Learning Convolutional Neural Networks for Graphs論文筆記
- 9. 論文筆記：LCLR 2018 FastGCN: Fast Learning With Graph Convolutional Networks Via Important Sampling
- 10. 《Relational inductive biases, deep learning, and graph networks》論文解讀（轉載）