Extending One-Stage Detection with Open-World Proposals

Understand

In many applications, such as autonomous driving, hand manipulation, or robot navigation, object detection methods must be able to detect objects unseen in the training set.

• Open World Detection(OWD) seeks to tackle this problem by generalizing detection performance to seen and unseen class categories.
• Recent works have seen success in the generation of class-agnostic proposals, which we call Open-World Proposals(OWP), but this comes at the cost of a big drop on the classification task when both tasks are considered in the detection model.
• These works have investigated two-stage Region Proposal Networks (RPN) by taking advantage of objectness scoring cues; however, for its simplicity, run-time, and decoupling of localization and classification, we investigate OWP through the lens of fully convolutional one-stage detection network, such as FCOS.