【圖像語義分割】Large Kernel Maters--Improved Semantic Segmentation by Global ConvNet
該篇文章是face++的文章，個人覺得相當嚴謹 摘要： 目前流行的網絡架構往往通過堆積小的卷積核（stack small filters），因爲在相同計算量下，stack small filters往往比large kernel更加有效。然而在圖像語義分割這塊，我們需要做一個dense per-pixle prediction， 我們發現大內核在同時實現localization和classific
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相關文章
- 1. 語義分割--Large Kernel Matters--Improve Semantic Segmentation by Global Convolutional Network
- 2. 語義分割之Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network
- 3. 圖像分割：GCN: Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network
- 4. 語義分割--Global Deconvolutional Networks for Semantic Segmentation
- 5. Semantic Segmentation---GCN:Large Kernel Matters---Improve Semantic Segmantic by Global ...（論文解讀四）
- 6. Large Kernel Matters ——Improve Semantic Segmentation by Global Convolutional Network論文筆記
- 7. PaperReading4-Large Kernel Matters—— Improve Semantic Segmentation by Global Convolutional Network
- 8. 【論文學習記錄】Large Kernel Matters-Improve Semantic Segmentation by Global Convolutional Network
- 9. Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network論文閱讀
- 10. Large Kernel Matters—— Improve Semantic Segmentation by Global Convolutional Network------論文理解