  Generalized zero-shot action recognition is a challenging problem, where the
task is to recognize new action categories that are unavailable during the
training stage, in addition to the seen action categories. Existing approaches
suffer from the inherent bias of the learned classifier towards the seen action
categories. As a consequence, unseen category samples are incorrectly
classified as belonging to one of the seen action categories. In this paper, we
set out to tackle this issue by arguing for a separate treatment of seen and
unseen action categories in generalized zero-shot action recognition. We
introduce an out-of-distribution detector that determines whether the video
features belong to a seen or unseen action category. To train our
out-of-distribution detector, video features for unseen action categories are
synthesized using generative adversarial networks trained on seen action
category features. To the best of our knowledge, we are the first to propose an
out-of-distribution detector based GZSL framework for action recognition in
videos. Experiments are performed on three action recognition datasets: Olympic
Sports, HMDB51 and UCF101. For generalized zero-shot action recognition, our
proposed approach outperforms the baseline (f-CLSWGAN) with absolute gains (in
classification accuracy) of 7.0%, 3.4%, and 4.9%, respectively, on these
datasets.
