Transcription of 1 DeepLab: Semantic Image Segmentation with Deep ...
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1 DeepLab: Semantic Image Segmentation with Deep ...
1. deeplab : Semantic Image Segmentation with deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen, George Papandreou, Senior Member, IEEE, Iasonas Kokkinos, Member, IEEE, Kevin Murphy, and Alan L. Yuille, Fellow, IEEE. Abstract In this work we address the task of Semantic Image Segmentation with deep Learning and make three main contributions [ ] 12 May 2017. that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within deep Convolutional Neural Networks.
arXiv:1606.00915v2 [cs.CV] 12 May 2017. 2 mance of our system, but comes at the cost of computing feature responses at all DCNN layers for multiple scaled versions of the input image. Instead, motivated by spatial pyramid pooling [19], [20], we propose a computationally
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