Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning

@article{Dong2022IncrementalDETRIF,
  title={Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning},
  author={Na Dong and Yongqiang Zhang and Ming Ding and Gim Hee Lee},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.04042}
}
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on incremental object detection that rely on the availability of abundant training samples per novel class that substantially limits the scalability to real-world setting where novel data can be scarce. In this paper, we propose the Incremental-DETR that does incremental few-shot object… 

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TLDR
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