  Action recognition is a fundamental capability for humanoid robots to
interact and cooperate with humans. This application requires the action
recognition system to be designed so that new actions can be easily added,
while unknown actions are identified and ignored. In recent years,
deep-learning approaches represented the principal solution to the Action
Recognition problem. However, most models often require a large dataset of
manually-labeled samples. In this work we target One-Shot deep-learning models,
because they can deal with just a single instance for class. Unfortunately,
One-Shot models assume that, at inference time, the action to recognize falls
into the support set and they fail when the action lies outside the support
set. Few-Shot Open-Set Recognition (FSOSR) solutions attempt to address that
flaw, but current solutions consider only static images and not sequences of
images. Static images remain insufficient to discriminate actions such as
sitting-down and standing-up. In this paper we propose a novel model that
addresses the FSOSR problem with a One-Shot model that is augmented with a
discriminator that rejects unknown actions. This model is useful for
applications in humanoid robotics, because it allows to easily add new classes
and determine whether an input sequence is among the ones that are known to the
system. We show how to train the whole model in an end-to-end fashion and we
perform quantitative and qualitative analyses. Finally, we provide real-world
examples.
