Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Video Face Recognition: Component-wise Feature Aggregation Network
  (C-FAN)   We propose a new approach to video face recognition. Our component-wise
feature aggregation network (C-FAN) accepts a set of face images of a subject
as an input, and outputs a single feature vector as the face representation of
the set for the recognition task. The whole network is trained in two steps:
(i) train a base CNN for still image face recognition; (ii) add an aggregation
module to the base network to learn the quality value for each feature
component, which adaptively aggregates deep feature vectors into a single
vector to represent the face in a video. C-FAN automatically learns to retain
salient face features with high quality scores while suppressing features with
low quality scores. The experimental results on three benchmark datasets,
YouTube Faces, IJB-A, and IJB-S show that the proposed C-FAN network is capable
of generating a compact feature vector with 512 dimensions for a video sequence
by efficiently aggregating feature vectors of all the video frames to achieve
state of the art performance.

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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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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: quant-ph
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The categories of this paper are: mtrl-th