  Hand gesture recognition allows humans to interact with machines
non-verbally, which has a huge application in underwater exploration using
autonomous underwater vehicles. Recently, a new gesture-based language called
CADDIAN has been devised for divers, and supervised learning methods have been
applied to recognize the gestures with high accuracy. However, such methods
fail when they encounter unseen gestures in real time. In this work, we
advocate the need for zero-shot underwater gesture recognition (ZSUGR), where
the objective is to train a model with visual samples of gestures from a few
``seen'' classes only and transfer the gained knowledge at test time to
recognize semantically-similar unseen gesture classes as well. After discussing
the problem and dataset-specific challenges, we propose new seen-unseen splits
for gesture classes in CADDY dataset. Then, we present a two-stage framework,
where a novel transformer learns strong visual gesture cues and feeds them to a
conditional generative adversarial network that learns to mimic feature
distribution. We use the trained generator as a feature synthesizer for unseen
classes, enabling zero-shot learning. Extensive experiments demonstrate that
our method outperforms the existing zero-shot techniques. We conclude by
providing useful insights into our framework and suggesting directions for
future research.
