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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    Title :

    Unsupervised Hyperbolic Action Recognition


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

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



    Publication date :

    2022-11-19


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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