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Object Detection with Grammar Models
Ross B Girshick · Pedro Felzenszwalb · David Mcallester

Wed Dec 14 08:45 AM -- 02:59 PM (PST) @

Compositional models provide an elegant formalism for representing the visual appearance of highly variable objects. While such models are appealing from a theoretical point of view, it has been difficult to demonstrate that they lead to performance advantages on challenging datasets. Here we develop a grammar model for person detection and show that it outperforms previous high-performance systems on the PASCAL benchmark. Our model represents people using a hierarchy of deformable parts, variable structure and an explicit model of occlusion for partially visible objects. To train the model, we introduce a new discriminative framework for learning structured prediction models from weakly-labeled data.

Author Information

Ross B Girshick (UC Berkeley)
Pedro Felzenszwalb (Brown University)
David Mcallester (Toyota Tech Institute Chicago)

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