N-RPN: Hard Example Learning for Region Proposal Networks. (arXiv:2208.01916v1 [cs.CV])

The region proposal task is to generate a set of candidate regions that
contain an object. In this task, it is most important to propose as many
candidates of ground-truth as possible in a fixed number of proposals. In a
typical image, however, there are too few hard negative examples compared to
the vast number of easy negatives, so region proposal networks struggle to
train on hard negatives. Because of this problem, networks tend to propose hard
negatives as candidates, …

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