Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: R-FCN-3000 at 30fps: Decoupling Detection and Classification   We present R-FCN-3000, a large-scale real-time object detector in which
objectness detection and classification are decoupled. To obtain the detection
score for an RoI, we multiply the objectness score with the fine-grained
classification score. Our approach is a modification of the R-FCN architecture
in which position-sensitive filters are shared across different object classes
for performing localization. For fine-grained classification, these
position-sensitive filters are not needed. R-FCN-3000 obtains an mAP of 34.9%
on the ImageNet detection dataset and outperforms YOLO-9000 by 18% while
processing 30 images per second. We also show that the objectness learned by
R-FCN-3000 generalizes to novel classes and the performance increases with the
number of training object classes - supporting the hypothesis that it is
possible to learn a universal objectness detector. Code will be made available.

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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat