Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cylindrical Convolutional Networks for Joint Object Detection and
  Viewpoint Estimation   Existing techniques to encode spatial invariance within deep convolutional
neural networks only model 2D transformation fields. This does not account for
the fact that objects in a 2D space are a projection of 3D ones, and thus they
have limited ability to severe object viewpoint changes. To overcome this
limitation, we introduce a learnable module, cylindrical convolutional networks
(CCNs), that exploit cylindrical representation of a convolutional kernel
defined in the 3D space. CCNs extract a view-specific feature through a
view-specific convolutional kernel to predict object category scores at each
viewpoint. With the view-specific feature, we simultaneously determine
objective category and viewpoints using the proposed sinusoidal soft-argmax
module. Our experiments demonstrate the effectiveness of the cylindrical
convolutional networks on joint object detection and viewpoint estimation.

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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
3
The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th