論文閱讀：Scene Graph Generation by Iterative Message Passing
Iterative Message Passing(CVPR2017) 文章 問題是最開始的graph怎麼得到？代碼裏還是用的full-connected graph作爲初始化。 本方法中，如果有C種關係，那麼網絡中是C+1個關係的概率，多的一個代表none-relation，物體類別也一樣，多出來一類代表background。 代碼裏，先將所有可能的relation都列出來了（任意兩
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相關文章
- 1. 論文閱讀：Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
- 2. 通過迭代消息傳遞生成場景圖Scene Graph Generation by Iterative Message Passing（2017）
- 3. 論文閱讀：Sequence Generation by Editing Prototype
- 4. 論文閱讀總結(Message Passing Attention Networks for Documents Understanding)
- 5. Graph R-CNN for Scene Graph Generation
- 6. 論文閱讀 Question Generation
- 7. 論文閱讀：Neural Motifs: Scene Graph Parsing with Global Context
- 8. SayAsYouWish:Fine-grained Control of Image Caption Generation with Abstract Scene Graphs ---- 論文閱讀筆記
- 9. 論文閱讀：Learning Human-Object Interactions by Graph Parsing Neural Networks
- 10. 論文閱讀：Talking Face Generation by Adversarially Disentangled Audio-Visual Representation