  Few-shot open-set recognition aims to classify both seen and novel images
given only limited training data of seen classes. The challenge of this task is
that the model is required not only to learn a discriminative classifier to
classify the pre-defined classes with few training data but also to reject
inputs from unseen classes that never appear at training time. In this paper,
we propose to solve the problem from two novel aspects. First, instead of
learning the decision boundaries between seen classes, as is done in standard
close-set classification, we reserve space for unseen classes, such that images
located in these areas are recognized as the unseen classes. Second, to
effectively learn such decision boundaries, we propose to utilize the
background features from seen classes. As these background regions do not
significantly contribute to the decision of close-set classification, it is
natural to use them as the pseudo unseen classes for classifier learning. Our
extensive experiments show that our proposed method not only outperforms
multiple baselines but also sets new state-of-the-art results on three popular
benchmarks, namely tieredImageNet, miniImageNet, and Caltech-USCD
Birds-200-2011 (CUB).
