  Recent advances in visual recognition show overarching success by virtue of
large amounts of supervised data. However,the acquisition of a large supervised
dataset is often challenging. This is also true for intelligent transportation
applications, i.e., traffic sign recognition. For example, a model trained with
data of one country may not be easily generalized to another country without
much data. We propose a novel feature embedding scheme for unseen class
classification when the representative class template is given. Traffic signs,
unlike other objects, have official images. We perform co-domain embedding
using a quadruple relationship from real and synthetic domains. Our quadruplet
network fully utilizes the explicit pairwise similarity relationships among
samples from different domains. We validate our method on three datasets with
two experiments involving one-shot classification and feature generalization.
The results show that the proposed method outperforms competing approaches on
both seen and unseen classes.
