  A practical large scale product recognition system suffers from the
phenomenon of long-tailed imbalanced training data under the E-commercial
circumstance at Alibaba. Besides product images at Alibaba, plenty of image
related side information (e.g. title, tags) reveal rich semantic information
about images. Prior works mainly focus on addressing the long tail problem in
visual perspective only, but lack of consideration of leveraging the side
information. In this paper, we present a novel side information based large
scale visual recognition co-training~(SICoT) system to deal with the long tail
problem by leveraging the image related side information. In the proposed
co-training system, we firstly introduce a bilinear word attention module
aiming to construct a semantic embedding over the noisy side information. A
visual feature and semantic embedding co-training scheme is then designed to
transfer knowledge from classes with abundant training data (head classes) to
classes with few training data (tail classes) in an end-to-end fashion.
Extensive experiments on four challenging large scale datasets, whose numbers
of classes range from one thousand to one million, demonstrate the scalable
effectiveness of the proposed SICoT system in alleviating the long tail
problem. In the visual search platform
Pailitao\footnote{http://www.pailitao.com} at Alibaba, we settle a practical
large scale product recognition application driven by the proposed SICoT
system, and achieve a significant gain of unique visitor~(UV) conversion rate.
