Abstract
Recognizing an activity with a single reference sample using metric learning approaches is a promising field research field. The majority of few-shot methods focus on object recognition or face-identification. We follow a metric learning approach to reduce the action recognition problem to a nearest neighbor search in embedding space. We encode signals on a signal level into images and then extract features using a deep residual CNN. Using triplet loss, we learn a feature embedding. The resulting encoder transforms features into an embedding space in which closer distances encode similar actions while higher distances encode different actions. Our approach based on a signal-level formulation remains flexible across a variety of modalities while outperforming the baseline on the large scale NTU RGB+D 120 dataset for the One-Shot action recognition protocol by 4.2%. Further, we show generalization on experiments using the UTD-MHAD dataset for inertial data and the Simitate dataset for motion capturing data. Furthermore, our inter-joint and inter-sensor experiments suggest good capabilities on previously unseen joint and sensor setups.
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URL
https://arxiv.org/abs/2004.11085
