  Convolutional neural networks have shown outstanding effectiveness in person
re-identification (re-ID). However, the models always have large number of
parameters and much computation for mobile application. In order to relieve
this problem, we propose a novel grafted network (GraftedNet), which is
designed by grafting a high-accuracy rootstock and a light-weighted scion. The
rootstock is based on the former parts of ResNet-50 to provide a strong
baseline, while the scion is a new designed module, composed of the latter
parts of SqueezeNet, to compress the parameters. To extract more discriminative
feature representation, a joint multi-level and part-based feature is proposed.
In addition, to train GraftedNet efficiently, we propose an accompanying
learning method, by adding an accompanying branch to train the model in
training and removing it in testing for saving parameters and computation. On
three public person re-ID benchmarks (Market1501, DukeMTMC-reID and CUHK03),
the effectiveness of GraftedNet are evaluated and its components are analyzed.
Experimental results show that the proposed GraftedNet achieves 93.02%, 85.3%
and 76.2% in Rank-1 and 81.6%, 74.7% and 71.6% in mAP, with only 4.6M
parameters.

Computer Vision and Pattern Recognition