Beyond Myopia: Enhancing Few-Shot Open-Set Recognition via Hyperopia Distillation

Existing few-shot open-set recognition (FSOR) methods primarily employ the meta-learning mechanism, in which each meta-task randomly selects a small subset of base classes as knowns, and samples an equal number of classes from the remaining base classes as pseudo-unknowns. While effective, these methods potentially face two critical weaknesses: i) Class-identity overlapping: The same classes are designated as knowns in one meta-task but may be considered as pseudo-unknowns in another, leading to conflicts across meta-tasks and consequently degrading the model’s performance; ii) Narrow pseudo-unknown utilization: Each meta-task selects only a limited number of base classes as pseudo-unknowns rather than providing a broader view of more available base classes. Fundamentally, these issues arise from the myopia of the meta-learners in existing methods, as they lack a broad view on all available base classes. To this end, we strategically propose a novel Hyperopia Distillation Enhancement framework (HDE) for FSOR, which encourages the meta-learner to observe a broader view of pseudo-unknown classes without too worrying about the class-imbalance issue, while effectively mitigating the class-identity overlapping problem. The key to HDE lies in its dual hyperopia distillation mechanism, which enhances the meta-learner by hyperopically distilling available full-view inter-class relationships and more pseudo-unknown knowledge. Extensive experiments verify the effectiveness of our HDE.