GCN_large kernel matters_improving semantic seg by global conv network
0. 寫作目的 好記性不如爛筆頭。 1. 主要思想 分割需要對像素進行分類，同時精確定位，而這兩個任務是存在矛盾的。 作者發現 large kernel conv對於分割更有效。 主要是 使用1 * k + k *1 和 k * 1 + 1 * k 的GCN。 2. 模型 3. 實驗結果 實驗結果看，效果提升很大。 從效果看，感覺 BR 的作用更大。 There ma
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相關文章
- 1. 語義分割之Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network
- 2. PaperReading4-Large Kernel Matters—— Improve Semantic Segmentation by Global Convolutional Network
- 3. 【論文學習記錄】Large Kernel Matters-Improve Semantic Segmentation by Global Convolutional Network
- 4. 語義分割--Large Kernel Matters--Improve Semantic Segmentation by Global Convolutional Network
- 5. Large Kernel Matters ——Improve Semantic Segmentation by Global Convolutional Network論文筆記
- 6. Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network論文閱讀
- 7. 圖像分割：GCN: Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network
- 8. Large Kernel Matters—— Improve Semantic Segmentation by Global Convolutional Network------論文理解
- 9. Semantic Segmentation---GCN:Large Kernel Matters---Improve Semantic Segmantic by Global ...（論文解讀四）
- 10. 【圖像語義分割】Large Kernel Maters--Improved Semantic Segmentation by Global ConvNet