AI RESEARCH
GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning
arXiv CS.CV
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ArXi:2604.17822v1 Announce Type: new Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Language-Image Pre-trained (CLIP) models have shown strong potential for CIL due to their powerful generalization ability. However, existing methods still face two key challenges: shared-parameter adaptation tends to cause old-knowledge drift, and task-specific knowledge organization often leads to poorly calibrated cross-task responses, making reliable routing difficult.