  As fine-grained visual classification (FGVC) being developed for decades,
great works related have exposed a key direction -- finding discriminative
local regions and revealing subtle differences. However, unlike identifying
visual contents within static images, for recognizing objects in the real
physical world, discriminative information is not only present within seen
local regions but also hides in other unseen perspectives. In other words, in
addition to focusing on the distinguishable part from the whole, for efficient
and accurate recognition, it is required to infer the key perspective with a
few glances, e.g., people may recognize a "Benz AMG GT" with a glance of its
front and then know that taking a look at its exhaust pipe can help to tell
which year's model it is. In this paper, back to reality, we put forward the
problem of active fine-grained recognition (AFGR) and complete this study in
three steps: (i) a hierarchical, multi-view, fine-grained vehicle dataset is
collected as the testbed, (ii) a simple experiment is designed to verify that
different perspectives contribute differently for FGVC and different categories
own different discriminative perspective, (iii) a policy-gradient-based
framework is adopted to achieve efficient recognition with active view
selection. Comprehensive experiments demonstrate that the proposed method
delivers a better performance-efficient trade-off than previous FGVC methods
and advanced neural networks.
