  In fine-grained visual categorization (FGVC), there is a near-singular focus
in pursuit of attaining state-of-the-art (SOTA) accuracy. This work carefully
analyzes the performance of recent SOTA methods, quantitatively, but more
importantly, qualitatively. We show that these models universally struggle with
certain "hard" images, while also making complementary mistakes. We underscore
the importance of such analysis, and demonstrate that combining complementary
models can improve accuracy on the popular CUB-200 dataset by over 5%. In
addition to detailed analysis and characterization of the errors made by these
SOTA methods, we provide a clear set of recommended directions for future FGVC
researchers.
