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Stationary Features and Folded Hierarchies for Efficient Object Detection

Stationary Features and Folded Hierarchies for Efficient Object Detection

This video was recorded at NIPS Workshop on Efficient Machine Learning, Whistler 2007. Most discriminative techniques for detecting instances from object categories in still images consist of looping over a partition of a pose space with dedicated binary classifiers. This strategy is inefficient for a complex pose, i.e., for fine-grained descriptions: i) fragmenting the training data, which is inevitable in dealing with high in-class variation, severely reduces accuracy; ii) the computational cost at high pose resolution is prohibitive due to visiting a massive pose partition. To overcome data-fragmentation I will discuss a novel framework centered on pose-indexed, stationary features, which allows for efficient, one-shot learning of pose-specific classifiers. Such features assign a... Show More
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