Abstract: Joint work with J. TigheI will present our work on image parsing, or
labeling each pixel in an image with its semantic category (e.g., sky,
ground, tree, person, etc.). Our aim is to achieve broad coverage across
hundreds of object categories in large-scale datasets that can
continuously evolve. I will first describe our baseline nonparametric
region-based parsing system that can easily scale to datasets with tens
of thousands of images and hundreds of labels. Next, I will describe our
approach to combining this region-based system with per-exemplar
sliding window detectors to improve parsing performance on small object
classes, which achieves state-of-the-art results on several challenging
datasets. Time and strength remaining, I may mention new extensions just
submitted to CVPR.
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