  Humans can watch a continuous video stream and effortlessly perform continual
acquisition and transfer of new knowledge with minimal supervision yet
retaining previously learnt experiences. In contrast, existing continual
learning (CL) methods require fully annotated labels to effectively learn from
individual frames in a video stream. Here, we examine a more realistic and
challenging problem$\unicode{x2014}$Label-Efficient Online Continual Object
Detection (LEOCOD) in streaming video. We propose a plug-and-play module,
Efficient-CLS, that can be easily inserted into and improve existing continual
learners for object detection in video streams with reduced data annotation
costs and model retraining time. We show that our method has achieved
significant improvement with minimal forgetting across all supervision levels
on two challenging CL benchmarks for streaming real-world videos. Remarkably,
with only 25% annotated video frames, our method still outperforms the base CL
learners, which are trained with 100% annotations on all video frames. The data
and source code will be publicly available at
https://github.com/showlab/Efficient-CLS.
