GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning
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. To address these issues, we propose GR4CIL, a framework combining task discrimination and knowledge routing for CLIP-based CIL. GR4CIL preserves task-specific visual knowledge ...
| Authors | Tianqi Wang, Jingcai Guo |
|---|---|
| Primary category | cs.CV |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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