Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the problem of efficiently annotating labels of objects when they are structured. Often the distribution over labels can be described using a joint potential function over the labels for which sampling is provably hard but efficient maximum a-posteriori (MAP) solvers exist. In this setting  develop novel entropy bounds that are based on the expected amount of perturbation to the potential function that is needed to change MAP decisions. By reasoning about the entropy reduction and cost tradeoff, our algorithm actively selects the next annotation task. As an example of our framework  propose a boundary refinement task which can used to obtain pixelaccurate image boundaries much faster than traditional tools by focussing on parts of the image for refinement in a multi-scale manner.
['Subhransu Maji', 'Tamir Hazan', 'Tommi S. Jaakkola']
I am writing a research paper on a idea Active Boundary Annotation using Random MAP Perturbations
[{"from": "human", "value": "I am writing a research paper on a idea Active Boundary Annotation using Random MAP Perturbations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the problem of efficiently annotating labels of objects when they are structured. Often the distribution over labels can be described using a joint potential function over the labels for which sampling is provably hard but efficient maximum a-posteriori (MAP) solvers exist. In this setting  develop novel entropy bounds that are based on the expected amount of perturbation to the potential function that is needed to change MAP decisions. By reasoning about the entropy reduction and cost tradeoff, our algorithm actively selects the next annotation task. As an example of our framework  propose a boundary refinement task which can used to obtain pixelaccurate image boundaries much faster than traditional tools by focussing on parts of the image for refinement in a multi-scale manner."}]