Details

Autor(en) / Beteiligte

• Castro-Vargas, John-Alejandro
• Garcia-Garcia, Alberto
• Martinez-Gonzalez, Pablo
• Oprea, Sergiu
• Garcia-Rodriguez, Jose
• Matellán, Vicente
• Heredia, Guillermo
• Tardioli, Danilo
• Silva, Manuel F.
• Marques, Lino

Titel

Unsupervised Hyperbolic Action Recognition

Ist Teil von

• ROBOT2022: Fifth Iberian Robotics Conference, p.479-488

Ort / Verlag

Cham: Springer International Publishing

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.