Abstract:Semantic segmentation has been actively studied with the great success of deep learning. Various network architectures for this task have been introduced over last few years, and the current stateof-the-art methods have shown significant advances enough to be applied to practical applications. However, previous methods often fail to segment small objects due to the limit on the spatial resolution of the feature map encoded through the deep network architecture. In this paper, we propose a simple yet effective … Show more
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