  The challenges of high intra-class variance yet low inter-class fluctuations
in fine-grained visual categorization are more severe with few labeled samples,
\textit{i.e.,} Fine-Grained categorization problems under the Few-Shot setting
(FGFS). High-order features are usually developed to uncover subtle differences
between sub-categories in FGFS, but they are less effective in handling the
high intra-class variance. In this paper, we propose a Target-Oriented
Alignment Network (TOAN) to investigate the fine-grained relation between the
target query image and support classes. The feature of each support image is
transformed to match the query ones in the embedding feature space, which
reduces the disparity explicitly within each category. Moreover, different from
existing FGFS approaches devise the high-order features over the global image
with less explicit consideration of discriminative parts, we generate
discriminative fine-grained features by integrating compositional concept
representations to global second-order pooling. Extensive experiments are
conducted on four fine-grained benchmarks to demonstrate the effectiveness of
TOAN compared with the state-of-the-art models.
