  Object recognition in the real-world requires handling long-tailed or even
open-ended data. An ideal visual system needs to recognize the populated head
visual concepts reliably and meanwhile efficiently learn about emerging new
tail categories with a few training instances. Class-balanced many-shot
learning and few-shot learning tackle one side of this problem, by either
learning strong classifiers for head or learning to learn few-shot classifiers
for the tail. In this paper, we investigate the problem of generalized few-shot
learning (GFSL) -- a model during the deployment is required to learn about
tail categories with few shots and simultaneously classify the head classes. We
propose the ClAssifier SynThesis LEarning (CASTLE), a learning framework that
learns how to synthesize calibrated few-shot classifiers in addition to the
multi-class classifiers of head classes with a shared neural dictionary,
shedding light upon the inductive GFSL. Furthermore, we propose an adaptive
version of CASTLE (ACASTLE) that adapts the head classifiers conditioned on the
incoming tail training examples, yielding a framework that allows effective
backward knowledge transfer. As a consequence, ACASTLE can handle GFSL with
classes from heterogeneous domains effectively. CASTLE and ACASTLE demonstrate
superior performances than existing GFSL algorithms and strong baselines on
MiniImageNet as well as TieredImageNet datasets. More interestingly, they
outperform previous state-of-the-art methods when evaluated with standard
few-shot learning criteria.
