論文記錄：Neural Motifs: Scene Graph Parsing with Global Context (CVPR-18)
（這裏只是記錄了論文的一些內容以及自己的一點點淺薄的理解，具體實驗尚未恢復。由於本人新人一枚，若有錯誤以及不足之處，還望不吝賜教） 總結 本文關注的問題是 Scene Graph 的生成。通過觀察 VG 數據集發現： 超過大半的 images，實體對之間的可能關係高度依賴於實體對的標籤，反之不是（object labels are highly predictive of relation lab
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相關文章
- 1. 論文閱讀：Neural Motifs: Scene Graph Parsing with Global Context
- 2. [GCPNet]Scene Parsing with Global Context Embedding
- 3. 論文導讀｜Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
- 4. Graph-based Dependency Parsing with Graph Neural Networks
- 5. ACNet_ Adaptive Context Network for Scene Parsing
- 6. OCNet_Object Context Network for Scene parsing
- 7. 論文筆記——Permutohedral-GCN: Graph Convolutional Networks with Global Attention
- 8. 《Pyramid Scene Parsing Network》論文筆記
- 9. 論文閱讀理解 - Pyramid Scene Parsing Network
- 10. 論文閱讀：《Human Parsing with Contextualized Convolutional Neural Network》ICCV 2015