  Prevalent techniques in zero-shot learning do not generalize well to other
related problem scenarios. Here, we present a unified approach for conventional
zero-shot, generalized zero-shot and few-shot learning problems. Our approach
is based on a novel Class Adapting Principal Directions (CAPD) concept that
allows multiple embeddings of image features into a semantic space. Given an
image, our method produces one principal direction for each seen class. Then,
it learns how to combine these directions to obtain the principal direction for
each unseen class such that the CAPD of the test image is aligned with the
semantic embedding of the true class, and opposite to the other classes. This
allows efficient and class-adaptive information transfer from seen to unseen
classes. In addition, we propose an automatic process for selection of the most
useful seen classes for each unseen class to achieve robustness in zero-shot
learning. Our method can update the unseen CAPD taking the advantages of few
unseen images to work in a few-shot learning scenario. Furthermore, our method
can generalize the seen CAPDs by estimating seen-unseen diversity that
significantly improves the performance of generalized zero-shot learning. Our
extensive evaluations demonstrate that the proposed approach consistently
achieves superior performance in zero-shot, generalized zero-shot and
few/one-shot learning problems.
