[GCPNet]Scene Parsing with Global Context Embedding
Abstract 加州大学美熹德分校发表在ICCV 2017上的工作. 本文利用全局信息进行场景解析。训练基于场景相似度的网络来产生一张图片的全局信息特征关系，然后利用该信息产生全局和空间的先验知识。最后将这些先验知识作为全局上下文线索结合到分割网络中。实验辨明这种做法可以较少与全局信息相悖的假正例，最后在MIT ADE20k和PASCAL Context上达到了不错的性能. 本文的主要贡献有三点
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- 1. 论文记录：Neural Motifs: Scene Graph Parsing with Global Context (CVPR-18)
- 2. 论文阅读：Neural Motifs: Scene Graph Parsing with Global Context
- 3. Global Embedding 理解
- 4. ACNet_ Adaptive Context Network for Scene Parsing
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- 7. 论文翻译：Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
- 8. Question Answering with Subgraph Embedding
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相关文章
- 1. 论文记录：Neural Motifs: Scene Graph Parsing with Global Context (CVPR-18)
- 2. 论文阅读：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. 论文翻译：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. 论文导读｜Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing