《DeepLab v1：semantic image segmentation with deep convolutional nets and fully connected CRFs》论文笔记
1. 概述 导读：文章指出仅仅使用DCNNs网络的最后一层实现精确地语义分割是不足够的。为此，本篇文章的工作将DCNNs与概率图模型来共同解决分割精度的问题。文章新提出的这个方法在定位分割的边界上超过了之前的方法（当时），在VOC 2012数据集上取得了71.6%的IOU，GPU上速度为8 FPS（不含CRF）。 相比于传统的视觉算法(SIFT或HOG)，DCNNs以其end-to-end方式获得
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