Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Deep Representations for Scene Labeling with Semantic Context
  Guided Supervision   Scene labeling is a challenging classification problem where each input image
requires a pixel-level prediction map. Recently, deep-learning-based methods
have shown their effectiveness on solving this problem. However, we argue that
the large intra-class variation provides ambiguous training information and
hinders the deep models' ability to learn more discriminative deep feature
representations. Unlike existing methods that mainly utilize semantic context
for regularizing or smoothing the prediction map, we design novel supervisions
from semantic context for learning better deep feature representations. Two
types of semantic context, scene names of images and label map statistics of
image patches, are exploited to create label hierarchies between the original
classes and newly created subclasses as the learning supervisions. Such
subclasses show lower intra-class variation, and help CNN detect more
meaningful visual patterns and learn more effective deep features. Novel
training strategies and network structure that take advantages of such label
hierarchies are introduced. Our proposed method is evaluated extensively on
four popular datasets, Stanford Background (8 classes), SIFTFlow (33 classes),
Barcelona (170 classes) and LM+Sun datasets (232 classes) with 3 different
networks structures, and show state-of-the-art performance. The experiments
show that our proposed method makes deep models learn more discriminative
feature representations without increasing model size or complexity.

3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an