《Strip Pooling：Rethinking Spatial Pooling for Scene Parsing》论文笔记
代码地址：SPNet 1. 概述 导读：池化操作是在逐像素预测任务中获取较大感受野范围较为高效的做法，传统一般采取 N ∗ N N*N N∗N的正规矩形区域进行池化，在这篇文章中引入了一种新的池化策略，就是使用长条形的池化kernel来实现池化，即是池化的核心被重新设计为 N ∗ 1 , 1 ∗ N N*1,1*N N∗1,1∗N，从而构建了strip pooling操作。这个操作的引入使得网络可
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相关文章
- 1. cvpr2020_SPNet_ Strip Pooling: rethinking spatial Pooling for Scene Parsing
- 2. Strip Pooling条纹池化论文解读: Rethinking Spatial Pooling for Scene Parsing
- 3. Strip Pooling: Rethinking Spatial Pooling for Scene Parsing论文阅读&翻译
- 4. 【ML&DL】【skimming】Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
- 5. CVPR2020：语义分割Strip Pooling条形池化: Rethinking Spatial Pooling for Scene Parsing
- 6. Spatial Attention Network for Scene Parsing
- 7. SCNN(Spatial As Deep: Spatial CNN for Traffic Scene Understanding)论文笔记
- 8. 《Pyramid Scene Parsing Network》论文笔记
- 9. 论文阅读理解 - Pyramid Scene Parsing Network
- 10. 《DANet：Dual Attention Network for Scene Segmentation》论文笔记