GCN_large kernel matters_improving semantic seg by global conv network
0. 写作目的 好记性不如烂笔头。 1. 主要思想 分割需要对像素进行分类，同时精确定位，而这两个任务是存在矛盾的。 作者发现 large kernel conv对于分割更有效。 主要是 使用1 * k + k *1 和 k * 1 + 1 * k 的GCN。 2. 模型 3. 实验结果 实验结果看，效果提升很大。 从效果看，感觉 BR 的作用更大。 There ma
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相关文章
- 1. 语义分割之Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network
- 2. PaperReading4-Large Kernel Matters—— Improve Semantic Segmentation by Global Convolutional Network
- 3. 【论文学习记录】Large Kernel Matters-Improve Semantic Segmentation by Global Convolutional Network
- 4. 语义分割--Large Kernel Matters--Improve Semantic Segmentation by Global Convolutional Network
- 5. Large Kernel Matters ——Improve Semantic Segmentation by Global Convolutional Network论文笔记
- 6. Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network论文阅读
- 7. 图像分割：GCN: Large Kernel Matters —— Improve Semantic Segmentation by Global Convolutional Network
- 8. Large Kernel Matters—— Improve Semantic Segmentation by Global Convolutional Network------论文理解
- 9. Semantic Segmentation---GCN:Large Kernel Matters---Improve Semantic Segmantic by Global ...（论文解读四）
- 10. 【图像语义分割】Large Kernel Maters--Improved Semantic Segmentation by Global ConvNet