圖卷積：Multi-Label Image Recognition with Graph Convolutional Networks ∗
Abstract 多標籤圖像識別的任務是預測圖像中存在的一組目標標籤。因爲對象一般同時出如今圖像中，所以須要對標籤依賴關係進行建模以提升識別性能。爲了捕獲和探索這些重要的依賴關係，咱們提出了一種基於圖卷積網絡(GCN)的多標籤分類模型。該模型在對象標籤上構建有向圖，其中每一個節點(標籤)由標籤的詞嵌入表示，GCN學會將這個標籤圖映射到一組相互依賴的對象分類器。這些分類器應用於另外一個子網絡提取的圖
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相關文章
- 1. Multi-Label Image Recognition with Graph Convolutional Networks
- 2. 圖卷積Graph Convolutional Networks
- 3. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
- 4. 《Multi-Label Image Recognition with Graph Convolutional Networks》論文理解
- 5. Multi-Label Image Recognition with Graph Convolutional Networks 論文閱讀筆記
- 6. 曠視2019CVPR-Multi-Label Image Recognition with Graph Convolutional Networks
- 7. 論文筆記：Multi-Label Image Recognition with Graph Convolutional Networks
- 8. 圖卷積網絡GRAPH CONVOLUTIONAL NETWORKS
- 9. 【GCN】圖卷積網絡 Graph Convolutional Networks
- 10. 曠視CVPR2019圖卷積多標籤圖像識別Multi-Label Image Recognition with Graph Convolutional Networks論文詳解