Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Half a Percent of Labels is Enough: Efficient Animal Detection in UAV
  Imagery using Deep CNNs and Active Learning   We present an Active Learning (AL) strategy for re-using a deep Convolutional
Neural Network (CNN)-based object detector on a new dataset. This is of
particular interest for wildlife conservation: given a set of images acquired
with an Unmanned Aerial Vehicle (UAV) and manually labeled gound truth, our
goal is to train an animal detector that can be re-used for repeated
acquisitions, e.g. in follow-up years. Domain shifts between datasets typically
prevent such a direct model application. We thus propose to bridge this gap
using AL and introduce a new criterion called Transfer Sampling (TS). TS uses
Optimal Transport to find corresponding regions between the source and the
target datasets in the space of CNN activations. The CNN scores in the source
dataset are used to rank the samples according to their likelihood of being
animals, and this ranking is transferred to the target dataset. Unlike
conventional AL criteria that exploit model uncertainty, TS focuses on very
confident samples, thus allowing a quick retrieval of true positives in the
target dataset, where positives are typically extremely rare and difficult to
find by visual inspection. We extend TS with a new window cropping strategy
that further accelerates sample retrieval. Our experiments show that with both
strategies combined, less than half a percent of oracle-provided labels are
enough to find almost 80% of the animals in challenging sets of UAV images,
beating all baselines by a margin.

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The categories of this paper are: math
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: physics
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th