Multi-Level Active Prediction of Useful Image Annotations for Recognition

NeurIPS 2008 Sudheendra VijayanarasimhanKristen Grauman

We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the category-learner to strategically choose what annotations it receives---based on both the expected reduction in uncertainty as well as the relative costs of obtaining each annotation... (read more)

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