Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Modulated Loss for Rotated Object Detection   Popular rotated detection methods usually use five parameters (coordinates of
the central point, width, height, and rotation angle) to describe the rotated
bounding box and l1-loss as the loss function. In this paper, we argue that the
aforementioned integration can cause training instability and performance
degeneration, due to the loss discontinuity resulted from the inherent
periodicity of angles and the associated sudden exchange of width and height.
This problem is further pronounced given the regression inconsistency among
five parameters with different measurement units. We refer to the above issues
as rotation sensitivity error (RSE) and propose a modulated rotation loss to
dismiss the loss discontinuity. Our new loss is combined with the
eight-parameter regression to further solve the problem of inconsistent
parameter regression. Experiments show the state-of-art performances of our
method on the public aerial image benchmark DOTA and UCAS-AOD. Its
generalization abilities are also verified on ICDAR2015, HRSC2016, and FDDB.
Qualitative improvements can be seen in Fig 1, and the source code will be
released with the publication of the paper.

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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol