Keywords: object detection, invariant features, hierarchical search Abstract: This talk is about research in scene interpretation. Most algorithms for detecting and describing instances from object categories consist of looping over a partition of a "pose space" with dedicated binary classifiers. This strategy is inefficient for a complex pose: fragmenting the training data severely reduces accuracy, and the computational cost is prohibitive due to visiting a massive pose partition. To overcome data-fragmentation I will discuss a novel framework centered on pose-indexed features, which allows for efficient, one-shot learning of pose-specific classifiers. Such features are designed so that the probability distribution of the response is invariant if an object is actually present. I will illustrate these ideas by detecting and localizing cats in highly cluttered greyscale scenes. This is joint work with Francois Fleuret.