ProtoTail: Prototype-Guided Representation and Pseudo-Labeling for Long-Tailed Fine-Grained Generalized Category Discovery

Generalized Category Discovery (GCD) in fine-grained recognition is challenging because categories often differ only in subtle local cues, while long-tailed training provides uneven evidence for learning category-specific representations. When category support is limited, flexible local representations can introduce weakly supported cues, and unreliable predictions may be reinforced through pseudo supervision. We propose \sc ProtoTail, a part-aware GCD framework that couples support-aware local representation learning with reliability-aware pseudo-label selection. ProtoTail uses class-conditioned prototypes over shared latent parts to capture category-specific local evidence, while category-wise gating regularizes effective local-part capacity according to estimated category support. In parallel, separate known- and novel-category selection rules progressively incorporate reliable unlabeled samples using the validation signals available to each group. The two components complement each other: improved local representations support more reliable sample selection, while accepted samples further refine category-specific representations. Under long-tailed training, ProtoTail achieves \bf 75.42% All accuracy on CUB and \bf 68.29% on Stanford Cars, with its clearest benefits on low-frequency categories; under balanced training, it reaches \bf 79.58% and \bf 76.30%, respectively. These results support support-aware local regularization and progressive pseudo supervision as complementary mechanisms for fine-grained GCD under uneven category support. Our code is available at: https://anonymous.4open.science/r/ProtoTail-FC6E