论文记录：Neural Motifs: Scene Graph Parsing with Global Context (CVPR-18)
（这里只是记录了论文的一些内容以及自己的一点点浅薄的理解，具体实验尚未恢复。由于本人新人一枚，若有错误以及不足之处，还望不吝赐教） 总结 本文关注的问题是 Scene Graph 的生成。通过观察 VG 数据集发现： 超过大半的 images，实体对之间的可能关系高度依赖于实体对的标签，反之不是（object labels are highly predictive of relation lab
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相关文章
- 1. 论文阅读：Neural Motifs: Scene Graph Parsing with Global Context
- 2. [GCPNet]Scene Parsing with Global Context Embedding
- 3. 论文导读｜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. 论文笔记——Permutohedral-GCN: Graph Convolutional Networks with Global Attention
- 8. 《Pyramid Scene Parsing Network》论文笔记
- 9. 论文阅读理解 - Pyramid Scene Parsing Network
- 10. 论文阅读：《Human Parsing with Contextualized Convolutional Neural Network》ICCV 2015