Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to count with deep object features   Learning to count is a learning strategy that has been recently proposed in
the literature for dealing with problems where estimating the number of object
instances in a scene is the final objective. In this framework, the task of
learning to detect and localize individual object instances is seen as a harder
task that can be evaded by casting the problem as that of computing a
regression value from hand-crafted image features. In this paper we explore the
features that are learned when training a counting convolutional neural network
in order to understand their underlying representation. To this end we define a
counting problem for MNIST data and show that the internal representation of
the network is able to classify digits in spite of the fact that no direct
supervision was provided for them during training. We also present preliminary
results about a deep network that is able to count the number of pedestrians in
a scene.

2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys