End-to-End Comparative Attention Networks for Person Re-Identification

@article{Liu2016EndtoEndCA,
  title={End-to-End Comparative Attention Networks for Person Re-Identification},
  author={Hao Liu and Jiashi Feng and Meibin Qi and Jianguo Jiang and Shuicheng Yan},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  volume={26},
  pages={3492-3506}
}
Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem. One of the major challenges lies in the lack of spatial and temporal cues, which makes it difficult to deal with large variations of lighting conditions, viewing angles, body poses, and occlusions. Recently, several deep-learning-based person re-identification approaches have been proposed and achieved remarkable performance. However, most of those… 
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463 Citations
Highly Influential Citations
31
Background Citations
202
Methods Citations
124
Results Citations
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463 Citations

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A divide-and-unite deep network for person re-identification

A novel architecture of discriminative descriptor learning is proposed, which is based on the global feature and supplemented by part features, and which can achieve remarkable performance against numerous state-of-the-arts benchmarks.

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