118265 20220914095756.0 doi 10.1109/CVPRW53098.2021.00312 sideral 127884 ART-2021-127884 eng Sabater A. Universidad de Zaragoza One-shot action recognition in challenging therapy scenarios 2021 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. © 2021 IEEE. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion Santos L. Santos-Victor J. Bernardino A. Montesano L. Universidad de Zaragoza (orcid)0000-0003-1183-349X Murillo A.C. Universidad de Zaragoza (orcid)0000-0002-7580-9037 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. (2021), 2771-2779 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. workshops IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2160-7508 1639449 http://zaguan.unizar.es/record/118265/files/texto_completo.pdf Preprint 2385820 http://zaguan.unizar.es/record/118265/files/texto_completo.jpg?subformat=icon icon Preprint oai:zaguan.unizar.es:118265 articulos driver 2022-09-13-14:49:24 ARTICLE