  Few-shot learning (FSL) for action recognition is a challenging task of
recognizing novel action categories which are represented by few instances in
the training data. In a more generalized FSL setting (G-FSL), both seen as well
as novel action categories need to be recognized. Conventional classifiers
suffer due to inadequate data in FSL setting and inherent bias towards seen
action categories in G-FSL setting. In this paper, we address this problem by
proposing a novel ProtoGAN framework which synthesizes additional examples for
novel categories by conditioning a conditional generative adversarial network
with class prototype vectors. These class prototype vectors are learnt using a
Class Prototype Transfer Network (CPTN) from examples of seen categories. Our
synthesized examples for a novel class are semantically similar to real
examples belonging to that class and is used to train a model exhibiting better
generalization towards novel classes. We support our claim by performing
extensive experiments on three datasets: UCF101, HMDB51 and Olympic-Sports. To
the best of our knowledge, we are the first to report the results for G-FSL and
provide a strong benchmark for future research. We also outperform the
state-of-the-art method in FSL for all the aforementioned datasets.
