Papers › MetaFormer: A Unified Meta Framework for Fine-Grained Recognition
MetaFormer: A Unified Meta Framework for Fine-Grained Recognition
Qishuai Diao, Yi Jiang, Bin Wen, Jia Sun, Zehuan Yuan
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | NABirds | MetaFormer (MetaFormer-2,384) | Accuracy | 93.0% | #1 of 30 | Archive leaderboard | report |
| Image Classification | iNaturalist | MetaFormer (MetaFormer-2,384,extra_info) | Top 1 Accuracy | 83.4% | #4 of 19 | Archive leaderboard | report |
| Image Classification | iNaturalist | MetaFormer (MetaFormer-2,384) | Top 1 Accuracy | 80.4% | #7 of 19 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | MetaFormer (MetaFormer-2,384,extra_info) | Top-1 Accuracy | 88.7% | #5 of 60 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | MetaFormer (MetaFormer-2,384) | Top-1 Accuracy | 84.3% | #10 of 60 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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