Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Embedding Deep Metric for Person Re-identication A Study Against Large
  Variations   Person re-identification is challenging due to the large variations of pose,
illumination, occlusion and camera view. Owing to these variations, the
pedestrian data is distributed as highly-curved manifolds in the feature space,
despite the current convolutional neural networks (CNN)'s capability of feature
extraction. However, the distribution is unknown, so it is difficult to use the
geodesic distance when comparing two samples. In practice, the current deep
embedding methods use the Euclidean distance for the training and test. On the
other hand, the manifold learning methods suggest to use the Euclidean distance
in the local range, combining with the graphical relationship between samples,
for approximating the geodesic distance. From this point of view, selecting
suitable positive i.e. intra-class) training samples within a local range is
critical for training the CNN embedding, especially when the data has large
intra-class variations. In this paper, we propose a novel moderate positive
sample mining method to train robust CNN for person re-identification, dealing
with the problem of large variation. In addition, we improve the learning by a
metric weight constraint, so that the learned metric has a better
generalization ability. Experiments show that these two strategies are
effective in learning robust deep metrics for person re-identification, and
accordingly our deep model significantly outperforms the state-of-the-art
methods on several benchmarks of person re-identification. Therefore, the study
presented in this paper may be useful in inspiring new designs of deep models
for person re-identification.

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The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: math
7
The categories of this paper are: hep-th
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The categories of this paper are: q-fin