Large-scale live active learning: Training object detectors with crawled data and crowds

Active learning and crowdsourcing are promising ways to efficiently build up training sets for object recognition, but thus far techniques are tested in artificially controlled settings. Typically the vision researcher has already deter-mined the dataset’s scope, the labels “actively ” obtained are in fact already known, and/or the crowd-sourced col-lection process is iteratively fine-tuned. We present an ap-proach for live learning of object detectors, in which the system autonomously refines its models by actively request-ing crowd-sourced annotations on images crawled from the Web. To address the technical issues such a large-scale system entails, we introduce a novel part-based detector amenable to linear classifiers, and show how to identify its most uncertain instances in sub-linear time with a hashing-based solution. We demonstrate the approach with exper-iments of unprecedented scale and autonomy, and show it successfully improves the state-of-the-art for the most chal-lenging objects in the PASCAL benchmark. In addition, we show our detector competes well with popular nonlinear classifiers that are much more expensive to train. 1