  This study explores the emerging area of continual panoptic segmentation,
highlighting three key balances. First, we introduce past-class backtrace
distillation to balance the stability of existing knowledge with the
adaptability to new information. This technique retraces the features
associated with past classes based on the final label assignment results,
performing knowledge distillation targeting these specific features from the
previous model while allowing other features to flexibly adapt to new
information. Additionally, we introduce a class-proportional memory strategy,
which aligns the class distribution in the replay sample set with that of the
historical training data. This strategy maintains a balanced class
representation during replay, enhancing the utility of the limited-capacity
replay sample set in recalling prior classes. Moreover, recognizing that replay
samples are annotated only for the classes of their original step, we devise
balanced anti-misguidance losses, which combat the impact of incomplete
annotations without incurring classification bias. Building upon these
innovations, we present a new method named Balanced Continual Panoptic
Segmentation (BalConpas). Our evaluation on the challenging ADE20K dataset
demonstrates its superior performance compared to existing state-of-the-art
methods. The official code is available at
https://github.com/jinpeng0528/BalConpas.
