Data learning for human pose tracking

Caterina Buizza
One of the most pressing problems in data-driven models is how to include latent data information in the model-building process. This could help to reduce the amount of data required for training in machine learning applications. Pose tracking is a field currently dominated by data-driven models and where the collection of large, labeled datasets is difficult and time intensive. We believe this is an application that could benefit significantly from the inclusion of structure in...
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