| MetaFormer: A Unified Meta Framework for Fine-Grained Recognition | ✓ Link | 93.0% | MetaFormer
(MetaFormer-2,384) | 2022-03-05 |
| Fine-grained Visual Classification with High-temperature Refinement and Background Suppression | ✓ Link | 93.0% | HERBS | 2023-03-11 |
| A Novel Plug-in Module for Fine-Grained Visual Classification | ✓ Link | 92.8% | PIM | 2022-02-08 |
| Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification | ✓ Link | 92.5% | MPSA | 2024-08-16 |
| ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder | ✓ Link | 92.5% | ViT-NeT
(SwinV2-B) | 2022-07-17 |
| Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization | | 92.3% | CSQA-Net | 2024-03-15 |
| Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual Recognition | ✓ Link | 92.12% | I2-HOFI | 2024-10-20 |
| Multi-scale Activation, Refinement, and Aggregation: Exploring Diverse Cues for Fine-Grained Bird Recognition | | 92.0% | MDCM | 2025-04-12 |
| Universal Fine-grained Visual Categorization by Concept Guided Learning | ✓ Link | 91.7% | CGL | 2025-01-06 |
| SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization | ✓ Link | 91.2% | SR-GNN | 2022-09-05 |
| An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification | ✓ Link | 91.1% | FAL-ViT | 2025-01-28 |
| Structural feature enhanced transformer for fine-grained image recognition | | 91.1 | SFETrans | 2025-06-14 |
| Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification | ✓ Link | 91.0% | CAP | 2021-01-17 |
| Delving into Multimodal Prompting for Fine-grained Visual Classification | | 91.0% | MP-FGVC | 2023-09-16 |
| TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification | | 90.9% | TransIFC | 2022-12-31 |
| TransFG: A Transformer Architecture for Fine-grained Recognition | ✓ Link | 90.8% | TransFG | 2021-03-14 |
| Fine-Grained Visual Classification via Internal Ensemble Learning Transformer | ✓ Link | 90.8% | IELT | 2023-02-13 |
| End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition | ✓ Link | 90.3% | FVE | 2020-07-04 |
| Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition | | 90.1% | TPSKG | 2021-07-14 |
| Fixing the train-test resolution discrepancy | ✓ Link | 89.2% | FixSENet-154 | 2019-06-14 |
| Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization | ✓ Link | 88.6% | MGE-CNN | 2019-10-01 |
| Classification-Specific Parts for Improving Fine-Grained Visual Categorization | ✓ Link | 88.5% | CS-Parts | 2019-09-16 |
| Classification-Specific Parts for Improving Fine-Grained Visual Categorization | ✓ Link | 88.5% | CS-Part | 2019-09-16 |
| Learning Attentive Pairwise Interaction for Fine-Grained Classification | ✓ Link | 88.1% | API-Net | 2020-02-24 |
| Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition | | 87.9% | PAIRS | 2018-01-27 |
| Cross-X Learning for Fine-Grained Visual Categorization | | 86.4% | Cross-X | 2019-09-10 |
| Maximum-Entropy Fine Grained Classification | | 83.0% | MaxEnt-CNN | 2018-12-01 |
| Pairwise Confusion for Fine-Grained Visual Classification | ✓ Link | 82.79% | PC-DenseNet-161 | 2017-05-22 |
| Exploring Localization for Self-supervised Fine-grained Contrastive Learning | ✓ Link | 79.64% | BYOL+CVSA (ResNet-50) | 2021-06-30 |
| Bilinear CNNs for Fine-grained Visual Recognition | ✓ Link | 79.4% | Bilinear-CNN | 2015-04-29 |