Weakly supervised representation learning for sequential and composite actions.

Camera enabled AI-based personal assistants will need to recognize human actions in order to be safe and effective. Current machine learning approaches for action recognition require extensive datasets of annotated videos that depicting the actions to be recognized. Such datasets are expensive to acquire. The goal of this project is to decrease the annotation required to train viable action recognition systems.

Intern: 
He Zhao
Faculty Supervisor: 
Michael S. Brown
Province: 
Ontario
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