Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Path Region-Based Convolutional Neural Network for Accurate
  Detection of Unconstrained "Hard Faces"   Large-scale variations still pose a challenge in unconstrained face
detection. To the best of our knowledge, no current face detection algorithm
can detect a face as large as 800 x 800 pixels while simultaneously detecting
another one as small as 8 x 8 pixels within a single image with equally high
accuracy. We propose a two-stage cascaded face detection framework, Multi-Path
Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a
deep neural network with a classic learning strategy, to tackle this challenge.
The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes
faces at three different scales. It simultaneously utilizes three parallel
outputs of the convolutional feature maps to predict multi-scale candidate face
regions. The "atrous" convolution trick (convolution with up-sampled filters)
and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN
to further boost its performance. The second stage is a Boosted Forests
classifier, which utilizes deep facial features pooled from inside the
candidate face regions as well as deep contextual features pooled from a larger
region surrounding the candidate face regions. This step is included to further
remove hard negative samples. Experiments show that this approach achieves
state-of-the-art face detection performance on the WIDER FACE dataset "hard"
partition, outperforming the former best result by 9.6% for the Average
Precision.

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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: stat