Abstract

We approach the problem of fast detection and recognition of a large number (thousands) of object categories while training on a very limited amount of examples, usually one per category. Examples of this task include: (i) detection of retail products, where we have only one studio image of each product available for training; (ii) detection of brand logos; and (iii) detection of 3D objects and their respective poses within a static 2D image, where only a sparse subset of (partial) object views is available for training, with a single example for each view. Building a detector based on so few examples presents a signifificant challenge for the current top-performing (deep) learning based techniques, which require large amounts of data to train. Our approach for this task is based on a non-parametric probabilistic model for initial detection, CNN-based refifinement and temporal integration where applicable. We successfully demonstrate its usefulness in a variety of experiments on both existing and our own benchmarks achieving state-ofthe-art performance