• Corpus ID: 222133860

OLALA: Object-Level Active Learning Based Layout Annotation

@article{Shen2020OLALAOA,
  title={OLALA: Object-Level Active Learning Based Layout Annotation},
  author={Zejiang Shen and Jian Zhao and Melissa Dell and Yaoliang Yu and Weining Li},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.01762}
}
In layout object detection problems, the ground-truth datasets are constructed by annotating object instances individually. Yet active learning for object detection is typically conducted at the image level, not at the object level. Because objects appear with different frequencies across images, image-level active learning may be subject to over-exposure to common objects. This reduces the efficiency of human labeling. This work introduces an Object-Level Active Learning based Layout… 

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