Towards Open-Set Object Detection and Discovery

Understand

With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world.

• However, an issue with the current setting is that all the predicted unknown objects share the same category as "unknown", which require incremental learning via a human-in-the-loop approach to label novel classes.
• In order to address this problem, we present a new task, namely Open-Set Object Detection and Discovery (OSODD).
• This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort.