  Vehicle re-identification (re-ID) matches images of the same vehicle across
different cameras. It is fundamentally challenging because the dramatically
different appearance caused by different viewpoints would make the framework
fail to match two vehicles of the same identity. Most existing works solved the
problem by extracting viewpoint-aware feature via spatial attention mechanism,
which, yet, usually suffers from noisy generated attention map or otherwise
requires expensive keypoint labels to improve the quality. In this work, we
propose Viewpoint-aware Channel-wise Attention Mechanism (VCAM) by observing
the attention mechanism from a different aspect. Our VCAM enables the feature
learning framework channel-wisely reweighing the importance of each feature
maps according to the "viewpoint" of input vehicle. Extensive experiments
validate the effectiveness of the proposed method and show that we perform
favorably against state-of-the-arts methods on the public VeRi-776 dataset and
obtain promising results on the 2020 AI City Challenge. We also conduct other
experiments to demonstrate the interpretability of how our VCAM practically
assists the learning framework.
