Facing the Hard Problems in FGVC
June 25, 2020
Connor Anderson MS Thesis Defense/PhD Qualifying Process
Connor Anderson MS Thesis Defense/PhD Qualifying Process July 10th at 10:00 AM via Zoom Advisor: Ryan Farrell
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