FOCUS: Bridging Fine-Grained Recognition and Open-World Discovery across Domains

We introduce the first unified framework for Fine-Grained Domain-Generalized Generalized Category Discovery (FG-DG-GCD), bringing open-world recognition closer to real-world deployment under domain shift. Unlike conventional GCD, which assumes labeled and unlabeled data come from the same distribution, DG-GCD learns only from labeled source data and must both recognize known classes and discover novel ones in unseen, unlabeled target domains. This problem is especially challenging in fine-grained settings, where subtle inter-class differences and large intra-class variation make domain generalization significantly harder. To support systematic evaluation, we establish the first FG-DG-GCD benchmarks by creating identity-preserving painting and sketch domains for CUB-200-2011, Stanford Cars, and FGVC-Aircraft using controlled diffusion-adapter stylization. On top of this ,we propose FoCUS, a single-stage framework that combines Domain-Consistent Parts Discovery (DCPD) for geometry-stable part reasoning with Uncertainty-Aware Feature Augmentation (UFA) for confidence-calibrated feature regularization through uncertainty-guided perturbations. Extensive experiments show that FoCUS outperforms strong GCD, FG-GCD, and DG-GCD baselines by 3.28%, 9.68%, and 2.07%, respectively, in clustering accuracy on the proposed benchmarks. It also remains competitive on coarse-grained DG-GCD tasks while achieving nearly 3x higher computational efficiency than the current state of the art. ^[Code and datasets will be released upon acceptance.]