論文閱讀：Learning Human-Object Interactions by Graph Parsing Neural Networks
GPNN(ECCV2018) 文章 用來檢測human-object interaction。Paper說提出GPNN的目的是爲了在HOI任務中顯式地對空間、時間（視頻）以及human-object關係進行利用。 complete HOI指的是所有的node之間都有link，node包括human和object，假設node特徵爲d_V維，edge的特徵爲d_E維（從兩個box的union
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相關文章
- 1. 論文閱讀筆記《Few-Shot Learning with Graph Neural Networks》
- 2. 論文導讀｜Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
- 3. 論文閱讀：（AFM）Learning the Weight of Feature Interactions via Attention Networks
- 4. Machine Learning & Deep Learning 論文閱讀筆記
- 5. Graph-based Dependency Parsing with Graph Neural Networks
- 6. 論文閱讀：Neural Motifs: Scene Graph Parsing with Global Context
- 7. 論文閱讀 Prototypical Networks for Few-shot Learning
- 8. Applications of Graph Neural Networks
- 9. [論文解讀]SuperGlue: Learning Feature Matching with Graph Neural Networks
- 10. Few-Shot Learning with Graph Neural Networks