《Multi-Label Image Recognition with Graph Convolutional Networks》论文理解
0.动机 多标签图像识别的两个重要问题： （1）如何有效获取目标标签之间的相关性？ （2）如何利用这些标签相关性提高分类表现？ 做者使用图（graph）来对标签之间的相互依赖关系进行建模，来灵活地获取标签空间中的拓扑结构： （1）因为从词嵌入向量到分类器的映射参数在全部类别中是共享的，因此学习到的分类器可以在词嵌入空间中（语义相关的概念在词嵌入空间中彼此临近）保留较弱的语义结构。与此同时，对于能够
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相关文章
- 1. Multi-Label Image Recognition with Graph Convolutional Networks
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- 7. 论文翻译：Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
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- 9. 旷视2019CVPR-Multi-Label Image Recognition with Graph Convolutional Networks
- 10. 图卷积：Multi-Label Image Recognition with Graph Convolutional Networks ∗