論文筆記：Multi-Label Image Recognition with Graph Convolutional Networks
論文：Multi-Label Image Recognition with Graph Convolutional Networks 通過data-driven方式建立有向圖用於描述標籤之間的關聯關係，提出了基於圖卷積（GCN）的端到端多標籤分類系統。通過ResNet101對圖像進行特徵提取，通過GCN學習到標籤映射作爲標籤分類器對圖像的特徵向量進行分類。 1、Introduction 對於多標籤
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相關文章
- 1. Multi-Label Image Recognition with Graph Convolutional Networks 論文筆記
- 2. Multi-Label Image Recognition with Graph Convolutional Networks 論文閱讀筆記
- 3. 論文筆記：Multi-Label Image Recognition with Graph Convolutional Networks
- 4. Multi-Label Image Recognition with Graph Convolutional Networks
- 5. 《Multi-Label Image Recognition with Graph Convolutional Networks》論文理解
- 6. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
- 7. 論文筆記：LCML 2019 Simplifying Graph Convolutional Networks
- 8. 論文筆記：Very Deep Convolutional Networks for Large-Scale Image Recognition
- 9. 曠視2019CVPR-Multi-Label Image Recognition with Graph Convolutional Networks
- 10. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition--論文筆記