  Person re-identification (re-ID) aims to retrieve the same person across
different cameras. In practice, it still remains a challenging task due to
background clutter, variations on body poses and view conditions, inaccurate
bounding box detection, etc. To tackle these issues, in this paper, we propose
a novel multi-receptive field attention (MRFA) module that utilizes filters of
various sizes to help network focusing on informative pixels. Besides, we
present a view-specific mechanism that guides attention module to handle the
variation of view conditions. Moreover, we introduce a Gaussian horizontal
random cropping/padding method which further improves the robustness of our
proposed network. Comprehensive experiments demonstrate the effectiveness of
each component. Our method achieves 95.5% / 88.1% in rank-1 / mAP on
Market-1501, 88.9% / 80.0% on DukeMTMC-reID, 81.1% / 78.8% on CUHK03 labeled
dataset and 78.9% / 75.3% on CUHK03 detected dataset, outperforming current
state-of-the-art methods.
