This paper studies the problem of continual learning in an open-world
scenario, referred to as Open-world Continual Learning (OwCL). OwCL is
increasingly rising while it is highly challenging in two-fold: i) learning a
sequence of tasks without forgetting knowns in the past, and ii) identifying
unknowns (novel objects/classes) in the future. Existing OwCL methods suffer
from the adaptability of task-aware boundaries between knowns and unknowns, and
do not consider the mechanism of knowledge transfer. In this work, we propose
Pro-KT, a novel prompt-enhanced knowledge transfer model for OwCL. Pro-KT
includes two key components: (1) a prompt bank to encode and transfer both
task-generic and task-specific knowledge, and (2) a task-aware open-set
boundary to identify unknowns in the new tasks. Experimental results using two
real-world datasets demonstrate that the proposed Pro-KT outperforms the
state-of-the-art counterparts in both the detection of unknowns and the
classification of knowns markedly.

这篇论文研究了持续学习在开放世界场景中的问题，即开放世界持续学习（OwCL）。作者提出了一种新的 Prompt 增强的知识转移模型 Pro-KT，该模型通过 Prompt 库对任务通用和任务特定知识进行编码和转移，并通过任务感知的开放集边界来识别新任务中的未知对象 / 类别，在两个真实数据集上的实验结果显示，所提出的 Pro-KT 在未知对象检测和已知对象分类方面均明显优于现有方法。