Researchers From ByteDance Introduce MetaFormer: A Unified Meta Framework for Fine-Grained Recognition That Achieves 92.3% and 92.7% on CUB-200-2011 and NABirds

Fine-grained visual classification, in contrast to generic object classification, tries to correctly classify things from the same basic category (birds, vehicles, etc.) into subcategories. Because of the modest inter-class variances and high intra-class variations, Fine-Grained Visual Classification (FGVC) has long been regarded as a difficult assignment.

The most common FGVC techniques, such as the part-based model and the attention-based model, are primarily focused on how to get the network to focus on the most discriminative regions. The inductive bias of …

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