  We present a generative framework for zero-shot action recognition where some
of the possible action classes do not occur in the training data. Our approach
is based on modeling each action class using a probability distribution whose
parameters are functions of the attribute vector representing that action
class. In particular, we assume that the distribution parameters for any action
class in the visual space can be expressed as a linear combination of a set of
basis vectors where the combination weights are given by the attributes of the
action class. These basis vectors can be learned solely using labeled data from
the known (i.e., previously seen) action classes, and can then be used to
predict the parameters of the probability distributions of unseen action
classes. We consider two settings: (1) Inductive setting, where we use only the
labeled examples of the seen action classes to predict the unseen action class
parameters; and (2) Transductive setting which further leverages unlabeled data
from the unseen action classes. Our framework also naturally extends to
few-shot action recognition where a few labeled examples from unseen classes
are available. Our experiments on benchmark datasets (UCF101, HMDB51 and
Olympic) show significant performance improvements as compared to various
baselines, in both standard zero-shot (disjoint seen and unseen classes) and
generalized zero-shot learning settings.
