  Prevailing deep convolutional neural networks (CNNs) for person
re-IDentification (reID) are usually built upon ResNet or VGG backbones, which
were originally designed for classification. Because reID is different from
classification, the architecture should be modified accordingly. We propose to
automatically search for a CNN architecture that is specifically suitable for
the reID task. There are three aspects to be tackled. First, body structural
information plays an important role in reID but it is not encoded in backbones.
Second, Neural Architecture Search (NAS) automates the process of architecture
design without human effort, but no existing NAS methods incorporate the
structure information of input images. Third, reID is essentially a retrieval
task but current NAS algorithms are merely designed for classification. To
solve these problems, we propose a retrieval-based search algorithm over a
specifically designed reID search space, named Auto-ReID. Our Auto-ReID enables
the automated approach to find an efficient and effective CNN architecture for
reID. Extensive experiments demonstrate that the searched architecture achieves
state-of-the-art performance while reducing 50% parameters and 53% FLOPs
compared to others.
