[GCPNet]Scene Parsing with Global Context Embedding
Abstract 加州大學美熹德分校發表在ICCV 2017上的工作. 本文利用全局信息進行場景解析。訓練基於場景相似度的網絡來產生一張圖片的全局信息特徵關係，然後利用該信息產生全局和空間的先驗知識。最後將這些先驗知識作爲全局上下文線索結合到分割網絡中。實驗辨明這種做法可以較少與全局信息相悖的假正例，最後在MIT ADE20k和PASCAL Context上達到了不錯的性能. 本文的主要貢獻有三點
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- 1. 論文記錄：Neural Motifs: Scene Graph Parsing with Global Context (CVPR-18)
- 2. 論文閱讀：Neural Motifs: Scene Graph Parsing with Global Context
- 3. Global Embedding 理解
- 4. ACNet_ Adaptive Context Network for Scene Parsing
- 5. OCNet_Object Context Network for Scene parsing
- 6. Context-Aware Network Embedding for Relation Modeling
- 7. 論文翻譯：Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
- 8. Question Answering with Subgraph Embedding
- 9. Contradiction Detection with Contradiction-Specific Word Embedding
- 10. 論文導讀｜Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
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相關文章
- 1. 論文記錄：Neural Motifs: Scene Graph Parsing with Global Context (CVPR-18)
- 2. 論文閱讀：Neural Motifs: Scene Graph Parsing with Global Context
- 3. Global Embedding 理解
- 4. ACNet_ Adaptive Context Network for Scene Parsing
- 5. OCNet_Object Context Network for Scene parsing
- 6. Context-Aware Network Embedding for Relation Modeling
- 7. 論文翻譯：Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
- 8. Question Answering with Subgraph Embedding
- 9. Contradiction Detection with Contradiction-Specific Word Embedding
- 10. 論文導讀｜Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing