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Learning Action Primitives for Multilevel Video Event Understanding Than Lan1, Lei Chen2, China Deng2, Guangdong Zhou2, and Greg Mori2 Stanford University1Simon Fraser University2Abstract. Human action

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Learning action primitives can be filled out by identifying the specific actions needed to accomplish a task, along with any relevant parameters or conditions.

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The purpose of learning action primitives is to improve the efficiency and effectiveness of machine learning algorithms by breaking down tasks into smaller, more manageable actions.

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Learning action primitives may include details such as the action name, description, inputs, outputs, constraints, and any relevant dependencies.

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