Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selective Feature Connection Mechanism: Concatenating Multi-layer CNN
  Features with a Feature Selector   Different layers of deep convolutional neural networks(CNNs) can encode
different-level information. High-layer features always contain more semantic
information, and low-layer features contain more detail information. However,
low-layer features suffer from the background clutter and semantic ambiguity.
During visual recognition, the feature combination of the low-layer and
high-level features plays an important role in context modulation. If directly
combining the high-layer and low-layer features, the background clutter and
semantic ambiguity may be caused due to the introduction of detailed
information. In this paper, we propose a general network architecture to
concatenate CNN features of different layers in a simple and effective way,
called Selective Feature Connection Mechanism (SFCM). Low-level features are
selectively linked to high-level features with a feature selector which is
generated by high-level features. The proposed connection mechanism can
effectively overcome the above-mentioned drawbacks. We demonstrate the
effectiveness, superiority, and universal applicability of this method on
multiple challenging computer vision tasks, including image classification,
scene text detection, and image-to-image translation.

10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
26
The categories of this paper are: bayes-an