  Fine-Grained Visual Classification(FGVC) is the task that requires
recognizing the objects belonging to multiple subordinate categories of a
super-category. Recent state-of-the-art methods usually design sophisticated
learning pipelines to tackle this task. However, visual information alone is
often not sufficient to accurately differentiate between fine-grained visual
categories. Nowadays, the meta-information (e.g., spatio-temporal prior,
attribute, and text description) usually appears along with the images. This
inspires us to ask the question: Is it possible to use a unified and simple
framework to utilize various meta-information to assist in fine-grained
identification? To answer this problem, we explore a unified and strong
meta-framework(MetaFormer) for fine-grained visual classification. In practice,
MetaFormer provides a simple yet effective approach to address the joint
learning of vision and various meta-information. Moreover, MetaFormer also
provides a strong baseline for FGVC without bells and whistles. Extensive
experiments demonstrate that MetaFormer can effectively use various
meta-information to improve the performance of fine-grained recognition. In a
fair comparison, MetaFormer can outperform the current SotA approaches with
only vision information on the iNaturalist2017 and iNaturalist2018 datasets.
Adding meta-information, MetaFormer can exceed the current SotA approaches by
5.9% and 5.3%, respectively. Moreover, MetaFormer can achieve 92.3% and 92.7%
on CUB-200-2011 and NABirds, which significantly outperforms the SotA
approaches. The source code and pre-trained models are released
athttps://github.com/dqshuai/MetaFormer.
