Fine-Grained Visual Classification (FGVC) involves distinguishing highly similar subordinate categories within the same basic-level class, presenting significant challenges due to subtle inter-class variations and substantial intra-class diversity. While Vision Transformer (ViT)-based approaches have demonstrated potential in this domain, they remain limited by two key issues: (1) the progressive loss of gradient-based edge and texture signals during hierarchical token aggregation and (2) insufficient extraction of discriminative fine-grained features. To overcome these limitations, we propose a Gradient-Aware Token Injection Transformer, a novel framework that explicitly incorporates gradient magnitude and orientation into token embeddings. This multi-modal feature fusion mechanism enhances the model’s capacity to preserve and leverage critical fine-grained visual cues. Extensive experiments on four standard FGVC benchmarks demonstrate the superiority of our approach, achieving 92.9% top-1 accuracy on CUB-200-2011, 90.5% on iNaturalist 2018, 93.2% on NABirds, and 95.3% on Stanford Cars, thereby validating its effectiveness and robustness.
Ma et al. (2026) studied this question.