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This work proposes an active basis model, a shared sketch algorithm, and a
computational architecture of sum-max maps for representing, learning, and
recognizing deformable templates.
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In a unified information theoretic framework, we learn hybrid image templates
composed of sketch and texture descriptors.
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Using this image representation we may discover visual objects under weak
supervision, i.e. without knowing the true locations, orientations and
categories of the visual objects.
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