Harnessing Textual Semantic Priors for Knowledge Transfer and Refinement in CLIP-Driven Continual Learning

The introduction of Semantic-Enriched Continual Adaptation (SECA) marks a significant advancement in continual learning, particularly for models like CLIP that have shown strong generalizability. Traditional approaches often overlook the semantic relevance of past knowledge, leading to challenges in maintaining a balance between stability and plasticity. SECA addresses these issues by utilizing textual semantic priors to guide knowledge transfer, thus reducing task interference. This innovative framework not only aims to enhance the anti-forgetting capabilities of models but also seeks to refine their adaptability to new tasks. As continual learning becomes increasingly vital in AI, the implications of SECA could lead to more robust and efficient learning systems, paving the way for advancements in various applications.