Image recognition with no or few instances by sharing attributes

We study the task of object recognition with no or few instances in its category. There are thousands of categories and only a few attributes. In other words, some attributes can be shared with different categories. In some case, it is difficult for some special categories to search enough positive instances. Even if it is easy for some category to search enough positive instances, it still waste time and resources.In this paper, we present an innovative image recognition method with no or few learning instances. We use attributes shared by other categories to learn the new category. It can make full use of the attributes and solve the problem that some categories do not have enough positive learning instances. Other objects or scenes in image also make contributions to improve recognition. At last, we set up a new database to do some experiments. The experimental results show our method's superiority. Although our method learns new category with no or few positive instances, it still keeps the high accuracy compared to traditional methods.