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.
tic segmentation typically employs a cascade of bottom-up image segmentation, followed by DCNN-based region classification. For instance the bounding box proposals and masked regions delivered by [47], [48] are used in [7] and [49] as inputs to a DCNN to incorporate shape information into the classification process. Similarly, the authors of [50]
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