  In the field of Class Incremental Object Detection (CIOD), creating models
that can continuously learn like humans is a major challenge. Pseudo-labeling
methods, although initially powerful, struggle with multi-scenario incremental
learning due to their tendency to forget past knowledge. To overcome this, we
introduce a new approach called Vision-Language Model assisted Pseudo-Labeling
(VLM-PL). This technique uses Vision-Language Model (VLM) to verify the
correctness of pseudo ground-truths (GTs) without requiring additional model
training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector.
Then, we generate custom queries for each pseudo GT using carefully designed
prompt templates that combine image and text features. This allows the VLM to
classify the correctness through its responses. Furthermore, VLM-PL integrates
refined pseudo and real GTs from upcoming training, effectively combining new
and old knowledge. Extensive experiments conducted on the Pascal VOC and MS
COCO datasets not only highlight VLM-PL's exceptional performance in
multi-scenario but also illuminate its effectiveness in dual-scenario by
achieving state-of-the-art results in both.
