Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning
HASTEN stabilizes Class-Incremental Learning using hierarchical knowledge to prevent catastrophic forgetting in CLIP-based models. It uses a semantic tree structure to organize visual and text features in hyperbolic space, ensuring fine-grained classes maintain their correct parent-child relationships across new tasks. This structured supervision reduces feature drift and improves performance on benchmarks.