Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting IM2GPS in the Deep Learning Era   Image geolocalization, inferring the geographic location of an image, is a
challenging computer vision problem with many potential applications. The
recent state-of-the-art approach to this problem is a deep image classification
approach in which the world is spatially divided into cells and a deep network
is trained to predict the correct cell for a given image. We propose to combine
this approach with the original Im2GPS approach in which a query image is
matched against a database of geotagged images and the location is inferred
from the retrieved set. We estimate the geographic location of a query image by
applying kernel density estimation to the locations of its nearest neighbors in
the reference database. Interestingly, we find that the best features for our
retrieval task are derived from networks trained with classification loss even
though we do not use a classification approach at test time. Training with
classification loss outperforms several deep feature learning methods (e.g.
Siamese networks with contrastive of triplet loss) more typical for retrieval
applications. Our simple approach achieves state-of-the-art geolocalization
accuracy while also requiring significantly less training data.

4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex