Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Local-Global Approach to Semantic Segmentation in Aerial Images   Aerial images are often taken under poor lighting conditions and contain low
resolution objects, many times occluded by other objects. In this domain,
visual context could be of great help, but there are still very few papers that
consider context in aerial image understanding and still remains an open
problem in computer vision. We propose a dual-stream deep neural network that
processes information along two independent pathways. Our model learns to
combine local and global appearance in a complementary way, such that together
form a powerful classifier. We test our dual-stream network on the task of
buildings segmentation in aerial images and obtain state-of-the-art results on
the Massachusetts Buildings Dataset. We study the relative importance of local
appearance versus the larger scene, as well as their performance in combination
on three new buildings datasets. We clearly demonstrate the effectiveness of
visual context in conjunction with deep neural networks for aerial image
understanding.

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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an