Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust and High Performance Face Detector   In recent years, face detection has experienced significant performance
improvement with the boost of deep convolutional neural networks. In this
report, we reimplement the state-of-the-art detector SRN and apply some tricks
proposed in the recent literatures to obtain an extremely strong face detector,
named VIM-FD. In specific, we exploit more powerful backbone network like
DenseNet-121, revisit the data augmentation based on data-anchor-sampling
proposed in PyramidBox, and use the max-in-out label and anchor matching
strategy in SFD. In addition, we also introduce the attention mechanism to
provide additional supervision. Over the most popular and challenging face
detection benchmark, i.e., WIDER FACE, the proposed VIM-FD achieves
state-of-the-art performance.

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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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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: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
35
The categories of this paper are: mtrl-th