  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.
