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06 October 2024 |
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Article overview
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Label-Efficient Online Continual Object Detection in Streaming Video | | Jay Zhangjie Wu
; David Junhao Zhang
; Wynne Hsu
; Mengmi Zhang
; Mike Zheng Shou
; | | Date: |
1 Jun 2022 | | Abstract: | To thrive in evolving environments, humans are capable of continual
acquisition and transfer of new knowledge, from a continuous video stream, with
minimal supervisions, while retaining previously learnt experiences. In
contrast to human learning, most standard continual learning benchmarks focus
on learning from static iid images in fully supervised settings. Here, we
examine a more realistic and challenging
problem$unicode{x2014}$Label-Efficient Online Continual Object Detection
(LEOCOD) in video streams. By addressing this problem, it would greatly benefit
many real-world applications with reduced annotation costs and retraining time.
To tackle this problem, we seek inspirations from complementary learning
systems (CLS) in human brains and propose a computational model, dubbed as
Efficient-CLS. Functionally correlated with the hippocampus and the neocortex
in CLS, Efficient-CLS posits a memory encoding mechanism involving
bidirectional interaction between fast and slow learners via synaptic weight
transfers and pattern replays. We test Efficient-CLS and competitive baselines
in two challenging real-world video stream datasets. Like humans, Efficient-CLS
learns to detect new object classes incrementally from a continuous temporal
stream of non-repeating video with minimal forgetting. Remarkably, with only
25% annotated video frames, our Efficient-CLS still leads among all comparative
models, which are trained with 100% annotations on all video frames. The data
and source code will be publicly available at
this https URL. | | Source: | arXiv, 2206.00309 | | Services: | Forum | Review | PDF | Favorites | |
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