Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Large-Scale Rendering of Simulated Crops for Synthetic Ground
  Truth Generation on Modular Supercomputers   Computer Vision problems deal with the semantic extraction of information
from camera images. Especially for field crop images, the underlying problems
are hard to label and even harder to learn, and the availability of
high-quality training data is low. Deep neural networks do a good job of
extracting the necessary models from training examples. However, they rely on
an abundance of training data that is not feasible to generate or label by
expert annotation. To address this challenge, we make use of the Unreal Engine
to render large and complex virtual scenes. We rely on the performance of
individual nodes by distributing plant simulations across nodes and both
generate scenes as well as train neural networks on GPUs, restricting node
communication to parallel learning.

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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat