DECF-FGVC: A discriminative enhancement and complementary fusion approach for fine-grained bird visual classification

Fine-grained bird image recognition plays a critical role in species conservation. However, existing approaches are constrained by complex background interference, insufficient extraction of discriminative features, and limited integration of hierarchical information. While Vision Transformers (ViTs) demonstrate superior performance over CNNs in fine-grained classification tasks, they remain vulnerable to background noise, with class tokens often failing to capture key regions - overlooking the complementarity between low-level details and high-level semantics. This study proposes DECF-FGVC, a novel model incorporating three modules: Patch Contrast Enhancement (PCE), Contrast Token Refiner (CTR), and Hierarchical Token Synthesizer (HTS). These modules synergistically suppress background noise, emphasize key regions, and integrate multi-layer features through attention-weighted image reconstruction, counterfactual learning-based token refinement, and hierarchical token fusion. Extensive experiments on CUB-200-2011, NABirds, and iNaturalist2017 datasets achieve classification accuracies of 91.9%, 91.4%, and 77.92% respectively, consistently outperforming state-of-the-art methods.