Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CenterNet: Keypoint Triplets for Object Detection   In object detection, keypoint-based approaches often suffer a large number of
incorrect object bounding boxes, arguably due to the lack of an additional look
into the cropped regions. This paper presents an efficient solution which
explores the visual patterns within each cropped region with minimal costs. We
build our framework upon a representative one-stage keypoint-based detector
named CornerNet. Our approach, named CenterNet, detects each object as a
triplet, rather than a pair, of keypoints, which improves both precision and
recall. Accordingly, we design two customized modules named cascade corner
pooling and center pooling, which play the roles of enriching information
collected by both top-left and bottom-right corners and providing more
recognizable information at the central regions, respectively. On the MS-COCO
dataset, CenterNet achieves an AP of 47.0%, which outperforms all existing
one-stage detectors by at least 4.9%. Meanwhile, with a faster inference speed,
CenterNet demonstrates quite comparable performance to the top-ranked two-stage
detectors. Code is available at https://github.com/Duankaiwen/CenterNet.

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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat