• Corpus ID: 22655199

Rethinking Atrous Convolution for Semantic Image Segmentation

@article{Chen2017RethinkingAC,
  title={Rethinking Atrous Convolution for Semantic Image Segmentation},
  author={Liang-Chieh Chen and George Papandreou and Florian Schroff and Hartwig Adam},
  journal={ArXiv},
  year={2017},
  volume={abs/1706.05587}
}
In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment… 
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