  We introduce a learning framework called learning using privileged
information (LUPI) to the computer vision field. We focus on the prototypical
computer vision problem of teaching computers to recognize objects in images.
We want the computers to be able to learn faster at the expense of providing
extra information during training time. As additional information about the
image data, we look at several scenarios that have been studied in computer
vision before: attributes, bounding boxes and image tags. The information is
privileged as it is available at training time but not at test time. We explore
two maximum-margin techniques that are able to make use of this additional
source of information, for binary and multiclass object classification. We
interpret these methods as learning easiness and hardness of the objects in the
privileged space and then transferring this knowledge to train a better
classifier in the original space. We provide a thorough analysis and comparison
of information transfer from privileged to the original data spaces for both
LUPI methods. Our experiments show that incorporating privileged information
can improve the classification accuracy. Finally, we conduct user studies to
understand which samples are easy and which are hard for human learning, and
explore how this information is related to easy and hard samples when learning
a classifier.

Computer Vision and Pattern Recognition;Machine Learning