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. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales.
rating richer information from context [45] and structured prediction techniques [22], [26], [27], [46], but the perfor-mance of these systems has always been compromised by the limited expressive power of the features. Over the past few years the breakthroughs of Deep Learning in image classification were quickly transferred to the semantic seg-
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