Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning
  of Maps   Image geolocalization is the task of identifying the location depicted in a
photo based only on its visual information. This task is inherently challenging
since many photos have only few, possibly ambiguous cues to their geolocation.
Recent work has cast this task as a classification problem by partitioning the
earth into a set of discrete cells that correspond to geographic regions. The
granularity of this partitioning presents a critical trade-off; using fewer but
larger cells results in lower location accuracy while using more but smaller
cells reduces the number of training examples per class and increases model
size, making the model prone to overfitting. To tackle this issue, we propose a
simple but effective algorithm, combinatorial partitioning, which generates a
large number of fine-grained output classes by intersecting multiple
coarse-grained partitionings of the earth. Each classifier votes for the
fine-grained classes that overlap with their respective coarse-grained ones.
This technique allows us to predict locations at a fine scale while maintaining
sufficient training examples per class. Our algorithm achieves the
state-of-the-art performance in location recognition on multiple benchmark
datasets.

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The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: eess
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an