Two-branch Objectness-centric Open World Detection

In recent years, with the development of deep learning, object detection has made great progress and has been widely used in many tasks. However, the previous models are all performed on closed sets, while there are many unknown categories in the real open world. Directly applying a model trained on known categories to the unknown classes will lead to misclassification. In this paper, we propose a two-branch objectness-centric open world object detection framework consisting of the bias-guided detector and the objectness-centric calibrator to effectively capture the objectness of both known and unknown instances and make the accurate prediction for known classes. The bias-guided detector trained with the known labels can predict the classes and boxes for known classes accurately. While the objectness-centric calibrator can localize the instances of any class, and does not affect the classification and regression of known classes. In the inference stage, we use the objectness-centric affirmation to confirm the results for known classes and predict the unknown instances. Comprehensive experiments conducted on the open world object detection benchmark validate the effectiveness of our method compared to state-of-the-art open world object detection approaches.