面向持续学习的适配器增强语义提示
Adapter-Enhanced Semantic Prompting for Continual Learning
AI总结：
本文提出轻量级持续学习框架 AESP，通过语义引导提示与适配器高效融合语义信息，并设计提示匹配机制，在三个数据集上验证了其有效性。
摘要：
持续学习（CL）使模型能够适应不断演变的数据流。CL 面临的一个主要挑战是灾难性遗忘，即新知识会覆盖先前获得的知识。传统方法通常保留过去的数据用于回放，或在模型中添加额外分支来学习新知识，这需要较高的内存开销。本文提出了一种新颖的轻量级 CL 框架——适配器增强语义提示（AESP），该框架融合了提示微调和适配器技术。具体而言，我们设计了语义引导提示以增强视觉特征的泛化能力，并利用适配器高效融合语义信息，旨在为持续学习任务学习更具适应性的特征。此外，为了选择合适的任务提示进行特征适应，我们开发了一种新颖的提示选择匹配机制。在三个 CL 数据集上的大量实验表明，我们的方法在多个指标上取得了良好的性能，展现了其推动 CL 发展的潜力。
英文摘要：
Continual learning (CL) enables models to adapt to evolving data streams. A major challenge of CL is catastrophic forgetting, where new knowledge will overwrite previously acquired knowledge. Traditional methods usually retain the past data for replay or add additional branches in the model to learn new knowledge, which has high memory requirements. In this paper, we propose a novel lightweight CL framework, Adapter-Enhanced Semantic Prompting (AESP), which integrates prompt tuning and adapter techniques. Specifically, we design semantic-guided prompts to enhance the generalization ability of visual features and utilize adapters to efficiently fuse the semantic information, aiming to learn more adaptive features for the continual learning task. Furthermore, to choose the right task prompt for feature adaptation, we have developed a novel matching mechanism for prompt selection. Extensive experiments on three CL datasets demonstrate that our approach achieves favorable performance across multiple metrics, showing its potential for advancing CL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
