Methods based on Deep Geometric Learning allow the development of solutions with a geometric approximation in different applications. In particular, the curved feature of hyperbolic space has the ability to describe hierarchical structures in a better manner. In this paper, we aim to define an unsupervised learning model for action recognition. The curved feature space is intended to be used to describe a hierarchical relationship between the clips that compose a complete video sequence. These, in turn, are related to each other by means of a triplet loss function and a VAE (Variational Auto-Encoder) neural architecture, which establishes a similarity relationship between clips to identify actions from a set of unlabelled data.


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    Titel :

    Unsupervised Hyperbolic Action Recognition


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Tardioli, Danilo (Herausgeber:in) / Matellán, Vicente (Herausgeber:in) / Heredia, Guillermo (Herausgeber:in) / Silva, Manuel F. (Herausgeber:in) / Marques, Lino (Herausgeber:in) / Castro-Vargas, John-Alejandro (Autor:in) / Garcia-Garcia, Alberto (Autor:in) / Martinez-Gonzalez, Pablo (Autor:in) / Oprea, Sergiu (Autor:in) / Garcia-Rodriguez, Jose (Autor:in)

    Kongress:

    Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022



    Erscheinungsdatum :

    2022-11-19


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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