DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution,and Fully CRFs
基於深度卷積網絡，空洞卷積和全連接CRFs的圖像語義分割（Deeplabv2）學習總結 Abstract 本文主要強調了基於深度學習語義分割的三大貢獻：1.atrous convolution（空洞卷積）能明確控制特徵響應分辨率和在不增大參數運算量的情況下增大卷積感受野獲得更多的上下文信息；2.atrous spatial pyramid pooling（空洞空間金字塔池化）探索了一種採用多采樣率
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