  One-shot action recognition aims to recognize new action categories from a
single reference example, typically referred to as the anchor example. This
work presents a novel approach for one-shot action recognition in the wild that
computes motion representations robust to variable kinematic conditions.
One-shot action recognition is then performed by evaluating anchor and target
motion representations. We also develop a set of complementary steps that boost
the action recognition performance in the most challenging scenarios. Our
approach is evaluated on the public NTU-120 one-shot action recognition
benchmark, outperforming previous action recognition models. Besides, we
evaluate our framework on a real use-case of therapy with autistic people.
These recordings are particularly challenging due to high-level artifacts from
the patient motion. Our results provide not only quantitative but also online
qualitative measures, essential for the patient evaluation and monitoring
during the actual therapy.
