Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SPGNet: Semantic Prediction Guidance for Scene Parsing   Multi-scale context module and single-stage encoder-decoder structure are
commonly employed for semantic segmentation. The multi-scale context module
refers to the operations to aggregate feature responses from a large spatial
extent, while the single-stage encoder-decoder structure encodes the high-level
semantic information in the encoder path and recovers the boundary information
in the decoder path. In contrast, multi-stage encoder-decoder networks have
been widely used in human pose estimation and show superior performance than
their single-stage counterpart. However, few efforts have been attempted to
bring this effective design to semantic segmentation. In this work, we propose
a Semantic Prediction Guidance (SPG) module which learns to re-weight the local
features through the guidance from pixel-wise semantic prediction. We find that
by carefully re-weighting features across stages, a two-stage encoder-decoder
network coupled with our proposed SPG module can significantly outperform its
one-stage counterpart with similar parameters and computations. Finally, we
report experimental results on the semantic segmentation benchmark Cityscapes,
in which our SPGNet attains 81.1% on the test set using only 'fine'
annotations.

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The categories of this paper are: math
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
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The categories of this paper are: math-ph
30
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: econ
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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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: cmp-lg
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The categories of this paper are: dg-ga