Tunable Hybrid Proposal Networks for the Open World

Understand

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 provide a flexible proposal solution that can be easily tuned to suit a variety of open-world settings.