Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Solution for Large-Scale Hierarchical Object Detection Datasets with
  Incomplete Annotation and Data Imbalance   This report demonstrates our solution for the Open Images 2018 Challenge.
Based on our detailed analysis on the Open Images Datasets (OID), it is found
that there are four typical features: large-scale, hierarchical tag system,
severe annotation incompleteness and data imbalance. Considering these
characteristics, an amount of strategies are employed, including SNIPER, soft
sampling, class-aware sampling (CAS), hierarchical non-maximum suppression
(HNMS) and so on. In virtue of these effective strategies, and further using
the powerful SENet154 armed with feature pyramid module and deformable ROIalign
as the backbone, our best single model could achieve a mAP of 56.9%. After a
further ensemble with 9 models, the final mAP is boosted to 62.2% in the public
leaderboard (ranked the 2nd place) and 58.6% in the private leaderboard (ranked
the 3rd place, slightly inferior to the 1st place by only 0.04 point).

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
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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: funct-an
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph