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Learning Human Activities and Object Affordances from RGB-D Videos
Given a RGB-D video, we jointly model the human activities and object affordances as a Markov random field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time.
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Skeleton Based Action Recognition
on CAD-120
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