Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters   We introduce a novel approach for keypoint detection task that combines
handcrafted and learned CNN filters within a shallow multi-scale architecture.
Handcrafted filters provide anchor structures for learned filters, which
localize, score and rank repeatable features. Scale-space representation is
used within the network to extract keypoints at different levels. We design a
loss function to detect robust features that exist across a range of scales and
to maximize the repeatability score. Our Key.Net model is trained on data
synthetically created from ImageNet and evaluated on HPatches benchmark.
Results show that our approach outperforms state-of-the-art detectors in terms
of repeatability, matching performance and complexity.

16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th