《Multi-Label Image Recognition with Graph Convolutional Networks》論文理解
0.動機 多標籤圖像識別的兩個重要問題： （1）如何有效獲取目標標籤之間的相關性？ （2）如何利用這些標籤相關性提高分類表現？ 做者使用圖（graph）來對標籤之間的相互依賴關係進行建模，來靈活地獲取標籤空間中的拓撲結構： （1）因爲從詞嵌入向量到分類器的映射參數在全部類別中是共享的，因此學習到的分類器可以在詞嵌入空間中（語義相關的概念在詞嵌入空間中彼此臨近）保留較弱的語義結構。與此同時，對於能夠
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相關文章
- 1. Multi-Label Image Recognition with Graph Convolutional Networks
- 2. Multi-Label Image Recognition with Graph Convolutional Networks 論文筆記
- 3. Multi-Label Image Recognition with Graph Convolutional Networks 論文閱讀筆記
- 4. 論文筆記：Multi-Label Image Recognition with Graph Convolutional Networks
- 5. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
- 6. Inverting Convolutional Networks with Convolutional Networks 論文理解
- 7. 論文翻譯：Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
- 8. Emotion Recognition Using Graph Convolutional Networks
- 9. 曠視2019CVPR-Multi-Label Image Recognition with Graph Convolutional Networks
- 10. 圖卷積：Multi-Label Image Recognition with Graph Convolutional Networks ∗