Kavli Affiliate: Wei Gao
| First 5 Authors: Xianghao Zang, Ge Li, Wei Gao, Xiujun Shu,
| Summary:
There are many challenging problems in the person re-identification (ReID)
task, such as the occlusion and scale variation. Existing works usually tried
to solve them by employing a one-branch network. This one-branch network needs
to be robust to various challenging problems, which makes this network
overburdened. This paper proposes to divide-and-conquer the ReID task. For this
purpose, we employ several self-supervision operations to simulate different
challenging problems and handle each challenging problem using different
networks. Concretely, we use the random erasing operation and propose a novel
random scaling operation to generate new images with controllable
characteristics. A general multi-branch network, including one master branch
and two servant branches, is introduced to handle different scenes. These
branches learn collaboratively and achieve different perceptive abilities. In
this way, the complex scenes in the ReID task are effectively disentangled, and
the burden of each branch is relieved. The results from extensive experiments
demonstrate that the proposed method achieves state-of-the-art performances on
three ReID benchmarks and two occluded ReID benchmarks. Ablation study also
shows that the proposed scheme and operations significantly improve the
performance in various scenes.
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