Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TILDE: A Temporally Invariant Learned DEtector   We introduce a learning-based approach to detect repeatable keypoints under
drastic imaging changes of weather and lighting conditions to which
state-of-the-art keypoint detectors are surprisingly sensitive. We first
identify good keypoint candidates in multiple training images taken from the
same viewpoint. We then train a regressor to predict a score map whose maxima
are those points so that they can be found by simple non-maximum suppression.
As there are no standard datasets to test the influence of these kinds of
changes, we created our own, which we will make publicly available. We will
show that our method significantly outperforms the state-of-the-art methods in
such challenging conditions, while still achieving state-of-the-art performance
on the untrained standard Oxford dataset.

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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: nlin