Encoder-DecoderwithAtrous Separable Convolution for ...
Encoder-Decoder with Atrous SeparableConvolution for Semantic Image SegmentationLiang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, andHartwig AdamGoogle Inc.{lcchen, yukun, gpapan, fschroff, pyramid pooling module or encode-decoder structureare used in deep neural networks for semantic segmentation networks are able to encode multi-scale contextual information byprobing the incoming features with filters or pooling operations at mul-tiple rates and multiple effective fields-of-view, while the latter networkscan capture sharper object boundaries by gradually recovering the spatialinformation. In this work, we propose to combine the advantages fromboth methods. Specifically, our proposed model, DeepLabv3+, extendsDeepLabv3 by adding a simple yet effective decoder module to refine thesegmentation results especially along object boundaries. We further ex-plore the Xception model and apply the depthwise Separable convolutionto both Atrous Spatial Pyramid Pooling and decoder modules, resultingin a faster and stronger encoder-decoder network.}
research/deeplab. 2 Related Work Models based on Fully Convolutional Networks (FCNs) [8,11] have demonstrated significant improvement on several segmentation benchmarks [1,2,3,4,5]. There are several model variants proposed to exploit the contextual information for segmentation [12,13,14,15,16,17,32,33], including those that employ multi-scale
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