Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Faster RER-CNN: application to the detection of vehicles in aerial
  images   Detecting small vehicles in aerial images is a difficult job that can be
challenging even for humans. Rotating objects, low resolution, small
inter-class variability and very large images comprising complicated
backgrounds render the work of photo-interpreters tedious and wearisome.
Unfortunately even the best classical detection pipelines like Faster R-CNN
cannot be used off-the-shelf with good results because they were built to
process object centric images from day-to-day life with multi-scale vertical
objects. In this work we build on the Faster R-CNN approach to turn it into a
detection framework that deals appropriately with the rotation equivariance
inherent to any aerial image task. This new pipeline (Faster Rotation
Equivariant Regions CNN) gives, without any bells and whistles,
state-of-the-art results on one of the most challenging aerial imagery
datasets: VeDAI and give good results w.r.t. the baseline Faster R-CNN on two
others: Munich and GoogleEarth .

24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th