Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from Web Data: the Benefit of Unsupervised Object Localization   Annotating a large number of training images is very time-consuming. In this
background, this paper focuses on learning from easy-to-acquire web data and
utilizes the learned model for fine-grained image classification in labeled
datasets. Currently, the performance gain from training with web data is
incremental, like a common saying "better than nothing, but not by much".
Conventionally, the community looks to correcting the noisy web labels to
select informative samples. In this work, we first systematically study the
built-in gap between the web and standard datasets, i.e. different data
distributions between the two kinds of data. Then, in addition to using web
labels, we present an unsupervised object localization method, which provides
critical insights into the object density and scale in web images.
Specifically, we design two constraints on web data to substantially reduce the
difference of data distributions for the web and standard datasets. First, we
present a method to control the scale, localization and number of objects in
the detected region. Second, we propose to select the regions containing
objects that are consistent with the web tag. Based on the two constraints, we
are able to process web images to reduce the gap, and the processed web data is
used to better assist the standard dataset to train CNNs. Experiments on
several fine-grained image classification datasets confirm that our method
performs favorably against the state-of-the-art methods.

18
The categories of this paper are: econ
3
The categories of this paper are: physics
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph