CP-Prompt：基于组合的跨模态提示用于域增量持续学习
CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning
AI总结：
本文提出CP-Prompt框架，通过组合式个性化提示捕获域内知识，并利用通用提示策略学习域间知识，以低参数训练实现跨模态域增量持续学习，在多个DIL任务上优于现有方法。
摘要：
跨模态域增量学习（DIL）的关键挑战在于使学习模型能够在同一任务下从具有不同特征分布的新数据中持续学习，同时不遗忘旧数据。然而，现有表现最好的方法由于缺乏域内知识提取和域间通用提示策略，仍然导致较高的遗忘率。本文提出一个简单而有效的框架CP-Prompt，通过训练有限参数来指导预训练模型学习新域并避免遗忘已有特征分布。CP-Prompt通过在多头自注意力层上组合插入个性化提示来捕获域内知识，然后通过通用提示策略学习域间知识。在三个广泛评估的DIL任务中，CP-Prompt相比最先进的基线方法表现出优越性。源代码可在 https://github.com/dannis97500/CP_Prompt 获取。
英文摘要：
The key challenge of cross-modal domain-incremental learning (DIL) is to enable the learning model to continuously learn from novel data with different feature distributions under the same task without forgetting old ones. However, existing top-performing methods still cause high forgetting rates, by lacking intra-domain knowledge extraction and inter-domain common prompting strategy. In this paper, we propose a simple yet effective framework, CP-Prompt, by training limited parameters to instruct a pre-trained model to learn new domains and avoid forgetting existing feature distributions. CP-Prompt captures intra-domain knowledge by compositionally inserting personalized prompts on multi-head self-attention layers and then learns the inter-domain knowledge with a common prompting strategy. CP-Prompt shows superiority compared with state-of-the-art baselines among three widely evaluated DIL tasks. The source code is available at https://github.com/dannis97500/CP_Prompt.
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。
