@inproceedings{goehring2014interactive,
    type = {inproceedings},
    key = {goehring2014interactive},
    title = {Interactive Adaptation of Real-Time Object Detectors},
    author = {Daniel Göhring and Judy Hoffman and Erik Rodner and Kate Saenko and Trevor Darrell},
    booktitle = {International Conference on Robotics and Automation (ICRA)},
    year = {2014},
    pages = {1282-1289},
    publisher = {IEEE},
    abstract = {In the following paper, we present a framework for quickly training 2D object detectors for robotic perception. Our method can be used by robotics practitioners to quickly (under 30 seconds per object) build a large-scale real-time perception system. In particular, we show how to create new detectors on the fly using large-scale internet image databases, thus allowing a user to choose among thousands of available categories to build a detection system suitable for the particular robotic application. Furthermore, we show how to adapt these models to the current environment with just a few in-situ images. Experiments on existing 2D benchmarks evaluate the speed, accuracy, and flexibility of our system.},
    groups = {adaptivelearning,sceneunderstanding,lifelonglearning},
    url = {http://raptor.berkeleyvision.org/},
}