Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Error Correction for Dense Semantic Image Labeling   Pixelwise semantic image labeling is an important, yet challenging, task with
many applications. Typical approaches to tackle this problem involve either the
training of deep networks on vast amounts of images to directly infer the
labels or the use of probabilistic graphical models to jointly model the
dependencies of the input (i.e. images) and output (i.e. labels). Yet, the
former approaches do not capture the structure of the output labels, which is
crucial for the performance of dense labeling, and the latter rely on carefully
hand-designed priors that require costly parameter tuning via optimization
techniques, which in turn leads to long inference times. To alleviate these
restrictions, we explore how to arrive at dense semantic pixel labels given
both the input image and an initial estimate of the output labels. We propose a
parallel architecture that: 1) exploits the context information through a
LabelPropagation network to propagate correct labels from nearby pixels to
improve the object boundaries, 2) uses a LabelReplacement network to directly
replace possibly erroneous, initial labels with new ones, and 3) combines the
different intermediate results via a Fusion network to obtain the final
per-pixel label. We experimentally validate our approach on two different
datasets for the semantic segmentation and face parsing tasks respectively,
where we show improvements over the state-of-the-art. We also provide both a
quantitative and qualitative analysis of the generated results.

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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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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: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: ao-sci