  Current approaches for fine-grained recognition do the following: First,
recruit experts to annotate a dataset of images, optionally also collecting
more structured data in the form of part annotations and bounding boxes.
Second, train a model utilizing this data. Toward the goal of solving
fine-grained recognition, we introduce an alternative approach, leveraging
free, noisy data from the web and simple, generic methods of recognition. This
approach has benefits in both performance and scalability. We demonstrate its
efficacy on four fine-grained datasets, greatly exceeding existing state of the
art without the manual collection of even a single label, and furthermore show
first results at scaling to more than 10,000 fine-grained categories.
Quantitatively, we achieve top-1 accuracies of 92.3% on CUB-200-2011, 85.4% on
Birdsnap, 93.4% on FGVC-Aircraft, and 80.8% on Stanford Dogs without using
their annotated training sets. We compare our approach to an active learning
approach for expanding fine-grained datasets.
