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Recent advances in visual recognition show overarching success by virtue oflarge amounts of supervised data. However,the acquisition of a large superviseddataset is often challenging. This is also true for intelligent transportationapplications, i.e., traffic sign recognition. For example, a model trained withdata of one country may not be easily generalized to another country withoutmuch data. We propose a novel feature embedding scheme for unseen classclassification when the representative class template is given. Traffic signs,unlike other objects, have official images. We perform co-domain embeddingusing a quadruple relationship from real and synthetic domains. Our quadrupletnetwork fully utilizes the explicit pairwise similarity relationships amongsamples from different domains. We validate our method on three datasets withtwo experiments involving one-shot classification and feature generalization.The results show that the proposed method outperforms competing approaches onboth seen and unseen classes.

Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition