Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RepPoints V2: Verification Meets Regression for Object Detection   Verification and regression are two general methodologies for prediction in
neural networks. Each has its own strengths: verification can be easier to
infer accurately, and regression is more efficient and applicable to continuous
target variables. Hence, it is often beneficial to carefully combine them to
take advantage of their benefits. In this paper, we take this philosophy to
improve state-of-the-art object detection, specifically by RepPoints. Though
RepPoints provides high performance, we find that its heavy reliance on
regression for object localization leaves room for improvement. We introduce
verification tasks into the localization prediction of RepPoints, producing
RepPoints v2, which provides consistent improvements of about 2.0 mAP over the
original RepPoints on the COCO object detection benchmark using different
backbones and training methods. RepPoints v2 also achieves 52.1 mAP on COCO
\texttt{test-dev} by a single model. Moreover, we show that the proposed
approach can more generally elevate other object detection frameworks as well
as applications such as instance segmentation. The code is available at
https://github.com/Scalsol/RepPointsV2.

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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
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The categories of this paper are: econ
10
The categories of this paper are: q-bio
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The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph