  We address the difficult problem of distinguishing fine-grained object
categories in low resolution images. Wepropose a simple an effective deep
learning approach that transfers fine-grained knowledge gained from high
resolution training data to the coarse low-resolution test scenario. Such
fine-to-coarse knowledge transfer has many real world applications, such as
identifying objects in surveillance photos or satellite images where the image
resolution at the test time is very low but plenty of high resolution photos of
similar objects are available. Our extensive experiments on two standard
benchmark datasets containing fine-grained car models and bird species
demonstrate that our approach can effectively transfer fine-detail knowledge to
coarse-detail imagery.
