One-shot action recognition in challenging therapy scenarios
This work addresses the problem of recognizing actions from a single example in real-world therapy settings, particularly for autistic patients, though it appears incremental as it builds on existing one-shot recognition methods.
The paper tackles one-shot action recognition in challenging therapy scenarios by developing a novel approach that computes robust motion representations and complementary steps to boost performance, achieving state-of-the-art results on the NTU-120 benchmark and providing quantitative and qualitative measures for therapy with autistic people.
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.