Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly-supervised multi-class object localization using only object
  counts as labels   We demonstrate the use of an extensive deep neural network to localize
instances of objects in images. The EDNN is naturally able to accurately
perform multi-class counting using only ground truth count values as labels.
Without providing any conceptual information, object annotations, or pixel
segmentation information, the neural network is able to formulate its own
conceptual representation of the items in the image. Using images labelled with
only the counts of the objects present,the structure of the extensive deep
neural network can be exploited to perform localization of the objects within
the visual field. We demonstrate that a trained EDNN can be used to count
objects in images much larger than those on which it was trained. In order to
demonstrate our technique, we introduce seven new data sets: five progressively
harder MNIST digit-counting data sets, and two datasets of 3d-rendered rubber
ducks in various situations. On most of these datasets, the EDNN achieves
greater than 99% test set accuracy in counting objects.

25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg