Lifelong Object Detection
- URL: http://arxiv.org/abs/2009.01129v1
- Date: Wed, 2 Sep 2020 15:08:51 GMT
- Title: Lifelong Object Detection
- Authors: Wang Zhou, Shiyu Chang, Norma Sosa, Hendrik Hamann, David Cox
- Abstract summary: We leverage the fact that new training classes arrive in a sequential manner and incrementally refine the model.
We consider the representative object detector, Faster R-CNN, for both accurate and efficient prediction.
- Score: 28.608982224098565
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in object detection have benefited significantly from rapid
developments in deep neural networks. However, neural networks suffer from the
well-known issue of catastrophic forgetting, which makes continual or lifelong
learning problematic. In this paper, we leverage the fact that new training
classes arrive in a sequential manner and incrementally refine the model so
that it additionally detects new object classes in the absence of previous
training data. Specifically, we consider the representative object detector,
Faster R-CNN, for both accurate and efficient prediction. To prevent abrupt
performance degradation due to catastrophic forgetting, we propose to apply
knowledge distillation on both the region proposal network and the region
classification network, to retain the detection of previously trained classes.
A pseudo-positive-aware sampling strategy is also introduced for distillation
sample selection. We evaluate the proposed method on PASCAL VOC 2007 and MS
COCO benchmarks and show competitive mAP and 6x inference speed improvement,
which makes the approach more suitable for real-time applications. Our
implementation will be publicly available.
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