HVGCD:Rethinking Generalized Category Discovery through Hypothesis–Verification

Generalized Category Discovery (GCD) aims to simultaneously recognize known categories and discover novel ones from partially labeled data without annotations. Existing methods rely on single-path global embeddings, implicitly performing only hypothesis formation and omitting an explicit verification mechanism, which leads to over-reliance on pretrained semantics and confident misclassification of unknown categories. We reformulate GCD as a Hypothesis–Verification representation framework and propose HVGCD, a structured representation framework that decomposes inference into a global hypothesis, hypothesisconditioned verification evidence, and statistical grounding. The verification branch reconstructs discriminative evidence via content-adaptive prototypes, enabling the model to verify and refine its hypotheses under open-world uncertainty, while a maximum-entropy grounding term stabilizes inference in open-world settings. As an efficient module, HVGCD integrates seamlessly into existing GCD pipelines and consistently improves performance on both coarseand fine-grained benchmarks.