Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond triplet loss: a deep quadruplet network for person
  re-identification   Person re-identification (ReID) is an important task in wide area video
surveillance which focuses on identifying people across different cameras.
Recently, deep learning networks with a triplet loss become a common framework
for person ReID. However, the triplet loss pays main attentions on obtaining
correct orders on the training set. It still suffers from a weaker
generalization capability from the training set to the testing set, thus
resulting in inferior performance. In this paper, we design a quadruplet loss,
which can lead to the model output with a larger inter-class variation and a
smaller intra-class variation compared to the triplet loss. As a result, our
model has a better generalization ability and can achieve a higher performance
on the testing set. In particular, a quadruplet deep network using a
margin-based online hard negative mining is proposed based on the quadruplet
loss for the person ReID. In extensive experiments, the proposed network
outperforms most of the state-of-the-art algorithms on representative datasets
which clearly demonstrates the effectiveness of our proposed method.

5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex