Abstract
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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Acknowledgment
We would like to thank “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the MoDeaAS project (grant PID2019-104818RB-I00). This work has also been supported by two Spanish national grants for PhD studies, FPU17/00166, and UAFPU2019-13 respectively. Furthermore, we would like to thank Nvidia for their generous hardware donation that made these experiments possible.
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Castro-Vargas, JA., Garcia-Garcia, A., Martinez-Gonzalez, P., Oprea, S., Garcia-Rodriguez, J. (2023). Unsupervised Hyperbolic Action Recognition. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-031-21062-4_39
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