Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Scene Understanding for Autonomous Driving   Most approaches for instance-aware semantic labeling traditionally focus on
accuracy. Other aspects like runtime and memory footprint are arguably as
important for real-time applications such as autonomous driving. Motivated by
this observation and inspired by recent works that tackle multiple tasks with a
single integrated architecture, in this paper we present a real-time efficient
implementation based on ENet that solves three autonomous driving related tasks
at once: semantic scene segmentation, instance segmentation and monocular depth
estimation. Our approach builds upon a branched ENet architecture with a shared
encoder but different decoder branches for each of the three tasks. The
presented method can run at 21 fps at a resolution of 1024x512 on the
Cityscapes dataset without sacrificing accuracy compared to running each task
separately.

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The categories of this paper are: q-bio
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: eess