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Improving Person Re-identification by Attribute and Identity Learning

Abstract · Mar 21, 2017 13:44 ·


Arxiv Abstract

  • Yutian Lin
  • Liang Zheng
  • Zhedong Zheng
  • Yu Wu
  • Yi Yang

Person re-identification (re-ID) and attribute recognition share a common target at the pedestrian description. Their difference consists in the granularity. Attribute recognition focuses on local aspects of a person while person re-ID usually extracts global representations. Considering their similarity and difference, this paper proposes a very simple convolutional neural network (CNN) that learns a re-ID embedding and predicts the pedestrian attributes simultaneously. This multi-task method integrates an ID classification loss and a number of attribute classification losses, and back-propagates the weighted sum of the individual losses. Albeit simple, we demonstrate on two pedestrian benchmarks that by learning a more discriminative representation, our method significantly improves the re-ID baseline and is scalable on large galleries. We report competitive re-ID performance compared with the state-of-the-art methods on the two datasets.

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