【論文閱讀】semantic image segmentation with deep convolutional nets and fully connected CRFs
文章的主要貢獻： 速度：帶atrous算法的DCNN可以保持8FPS的速度，全連接CRF平均推斷需要0.5s； 準確：在PASCAL語義分割挑戰中獲得了第二的成績； 簡單：DeepLab是由兩個非常成熟的模塊(DCNN和CRFs)級聯而成。 一、概述 自LeCun（1998）以來，DCNN一直被選作版面識別的方法，如今已經成爲高級視覺研究的主流，提高了計算機視覺性能，廣泛應用於圖像分割，對象檢測，
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相關文章
- 1. 文章閱讀：Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
- 2. 論文閱讀理解 - Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs
- 3. 《DeepLab v1：semantic image segmentation with deep convolutional nets and fully connected CRFs》論文筆記
- 4. Semantic Segmentation---DeepLab V1:Semantic Image Segmentation with Deep Convolutional Nets..（論文解讀二）
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