Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature-based Attention in Convolutional Neural Networks   Convolutional neural networks (CNNs) have proven effective for image
processing tasks, such as object recognition and classification. Recently, CNNs
have been enhanced with concepts of attention, similar to those found in
biology. Much of this work on attention has focused on effective serial spatial
processing. In this paper, I introduce a simple procedure for applying
feature-based attention (FBA) to CNNs and compare multiple implementation
options. FBA is a top-down signal applied globally to an input image which
aides in detecting chosen objects in cluttered or noisy settings. The concept
of FBA and the implementation details tested here were derived from what is
known (and debated) about biological object- and feature-based attention. The
implementations of FBA described here increase performance on challenging
object detection tasks using a procedure that is simple, fast, and does not
require additional iterative training. Furthermore, the comparisons performed
here suggest that a proposed model of biological FBA (the "feature similarity
gain model") is effective in increasing performance.

28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an