论文阅读：Learning Human-Object Interactions by Graph Parsing Neural Networks

时间 2020-12-24

标签 scene understand 繁體版

原文原文链接

GPNN(ECCV2018) 文章用来检测human-object interaction。Paper说提出GPNN的目的是为了在HOI任务中显式地对空间、时间（视频）以及human-object关系进行利用。 complete HOI指的是所有的node之间都有link，node包括human和object，假设node特征为d_V维，edge的特征为d_E维（从两个box的union

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1. 论文阅读笔记《Few-Shot Learning with Graph Neural Networks》
2. 论文导读｜Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
3. 论文阅读：（AFM）Learning the Weight of Feature Interactions via Attention Networks
4. Machine Learning & Deep Learning 论文阅读笔记
5. Graph-based Dependency Parsing with Graph Neural Networks
6. 论文阅读：Neural Motifs: Scene Graph Parsing with Global Context
7. 论文阅读 Prototypical Networks for Few-shot Learning
8. Applications of Graph Neural Networks
9. [论文解读]SuperGlue: Learning Feature Matching with Graph Neural Networks
10. Few-Shot Learning with Graph Neural Networks

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