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W3179667740 abstract "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." @default.
W3179667740 created "2021-07-19" @default.
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W3179667740 date "2021-06-01" @default.
W3179667740 modified "2023-10-01" @default.
W3179667740 title "One-shot action recognition in challenging therapy scenarios" @default.
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W3179667740 doi "https://doi.org/10.1109/cvprw53098.2021.00312" @default.
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