Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning a Weight Map for Weakly-Supervised Localization   In the weakly supervised localization setting, supervision is given as an
image-level label. We propose to employ an image classifier $f$ and to train a
generative network $g$ that outputs, given the input image, a per-pixel weight
map that indicates the location of the object within the image. Network $g$ is
trained by minimizing the discrepancy between the output of the classifier $f$
on the original image and its output given the same image weighted by the
output of $g$. The scheme requires a regularization term that ensures that $g$
does not provide a uniform weight, and an early stopping criterion in order to
prevent $g$ from over-segmenting the image. Our results indicate that the
method outperforms existing localization methods by a sizable margin on the
challenging fine-grained classification datasets, as well as a generic image
recognition dataset. Additionally, the obtained weight map is also
state-of-the-art in weakly supervised segmentation in fine-grained
categorization datasets.

31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph