  This paper presents experiments extending the work of Ba et al. (2014) on
recurrent neural models for attention into less constrained visual
environments, specifically fine-grained categorization on the Stanford Dogs
data set. In this work we use an RNN of the same structure but substitute a
more powerful visual network and perform large-scale pre-training of the visual
network outside of the attention RNN. Most work in attention models to date
focuses on tasks with toy or more constrained visual environments, whereas we
present results for fine-grained categorization better than the
state-of-the-art GoogLeNet classification model. We show that our model learns
to direct high resolution attention to the most discriminative regions without
any spatial supervision such as bounding boxes, and it is able to discriminate
fine-grained dog breeds moderately well even when given only an initial
low-resolution context image and narrow, inexpensive glimpses at faces and fur
patterns. This and similar attention models have the major advantage of being
trained end-to-end, as opposed to other current detection and recognition
pipelines with hand-engineered components where information is lost. While our
model is state-of-the-art, further work is needed to fully leverage the
sequential input.
