Tunable Hybrid Proposal Networks for the Open World

arXiv:2208.11050v3 Announce Type: replace
Abstract: Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail to provide high recall in open-world environments where important novel objects may be encountered. While a handful of recent works attempt to tackle this problem, they fail to consider that the optimal behavior of a proposal network can vary significantly depending on the data and application. Our goal is to …

abstract art arxiv cs.cv current environments hybrid learn meaning networks novel object objects open-world recall state training type world