Tree of Prompts: Aligning Hierarchical Visual Prior for Continual Generalized Category Discovery

Continual Generalized Category Discovery (C-GCD) aims to incrementally identify both known and novel classes from unlabeled data streams while preserving previously acquired knowledge. However, current approaches face a critical limitation we term unstructured knowledge interference, a critical issue that arises when unconstrained parameter updates entangle discriminative representations across classes, severely contaminating the feature space and introducing significant transfer and bias risks. To address these challenges, we propose the Tree of Prompts (ToP), a novel hierarchical prompting framework that facilitates structured knowledge adaptation through multi-granular parameter regulation. ToP hierarchically integrates three synergistic components: (1) Stage-level prompts preserve historical knowledge by isolating task-specific parameters, thereby mitigating conflicts between incremental tasks; (2) Centroid-level prompts disentangle category semantics through learnable prototype calibration, sharpening decision boundaries in the feature space; and (3) Context-level prompts dynamically capture discriminative local features to suppress contamination from superficial similarities. Experimental results demonstrate that ToP markedly outperforms existing methods and provides a comprehensive and efficient solution for C-GCD.