Wu, J. Z., Zhang, D. J., Hsu, W., Zhang, M., & Shou, M. Z. (2023, October 1). Label-Efficient Online Continual Object Detection in Streaming Video. 2023 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv51070.2023.01763
Abstract:
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—Label-Efficient Online Continual Object Detection (LEOCOD) in streaming video. We propose a plugand-play module, Efficient-CLS, that can be easily inserted
into and consistently improve existing CL algorithms 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 proposed method still outperforms the state-of-the-art CL models trained with 100% annotations on all video frames. The
data and source code will be publicly available at https:
//github.com/showlab/Efficient-CLS.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISG2-RP-2021-025
This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISGGC-2019-001-2A
This research / project is supported by the National Research Foundation - NRF Fellowship
Grant Reference no. : NRF-NRFF15-2023-0001
This research / project is supported by the National Research Foundation - NRF Fellowship
Grant Reference no. : NRF-NRFF13-2021-0008
This research / project is supported by the National University of Singapore - NUS Start-up grant
Grant Reference no. : N.A
This research / project is supported by the A*STAR - A*STAR Start-up
Grant Reference no. : N.A
This research / project is supported by the A*STAR - Early Career Investigatorship from Center for Frontier AI Research (CFAR),
Grant Reference no. : N.A