CLIPPan: Adapting CLIP as A Supervisor for Unsupervised Pansharpening

The paper titled 'Preserving Cross-Modal Consistency for CLIP-based Class-Incremental Learning' addresses the challenges of class-incremental learning (CIL) in vision-language models like CLIP. It introduces a two-stage framework called DMC, which separates the adaptation of the vision encoder from the optimization of textual soft prompts. This approach aims to mitigate classifier bias and maintain cross-modal alignment, enhancing the model's ability to learn new categories without forgetting previously acquired knowledge.

The article introduces NP-LoRA, a novel framework for Low-Rank Adaptation (LoRA) fusion that addresses the issue of interference in existing methods. Traditional weight-based merging often leads to one LoRA dominating another, resulting in degraded fidelity. NP-LoRA utilizes a projection-based approach to maintain subspace separation, thereby enhancing the quality of fusion by preventing structural interference among principal directions.