Project Abstract
One of the original goals of computer vision was to fully understand
a natural scene. This requires solving several sub-problems
simultaneously, including object detection, region labeling, and
geometric reasoning. The last few decades have seen great progress
in tackling each of these problems in isolation. Only recently have
researchers returned to the difficult task of considering them
jointly. In this work, we consider learning a set of related models
in such that they both solve their own problem and help each other.
We develop a framework called Cascaded Classification Models
(\OurMethod), where repeated instantiations of these classifiers are
coupled by their input/output variables in a cascade that improves
performance at each level. Our method requires only a limited
``black box'' interface with the models, allowing us to use very
sophisticated, state-of-the-art classifiers without having to look
under the hood. We demonstrate the effectiveness of our method on a
large set of natural images by combining the subtasks of scene
categorization, object detection, multiclass image segmentation, and
3d reconstruction.
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| (top) Output results from the
independent models, (bottom) Results from the integrated (CCM) model.
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