论文标题
面对FGVC中的严重问题
Facing the Hard Problems in FGVC
论文作者
论文摘要
在细粒度的视觉分类（FGVC）中，在追求达到最新的（SOTA）精度方面存在着近乎单位的重点。这项工作仔细地分析了最近的SOTA方法的性能，但更重要的是，定性。我们表明，这些模型在某些“硬”图像中普遍挣扎，同时也犯了互补的错误。我们强调了这种分析的重要性，并证明将互补模型组合可以提高流行的CUB-200数据集的准确性超过5％。除了对这些SOTA方法犯错的详细分析和表征外，我们还为未来的FGVC研究人员提供了一系列建议的方向。
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.
