A Framework for Open World Object Detection

Open World Object Detection (OWOD) is a computer vision task that focuses on real-world scenarios where object detection algorithms need to not only detect known and labeled objects but also handle novel and unknown objects that were not seen during training. This distinguishes OWOD from traditional object detection benchmarks, where the scope is limited to detecting only known object classes. The main challenge in OWOD lies in detecting and classifying unknown objects, which were not part of the training data. In standard object detection, objects not overlapping with labeled objects are automatically classified as background. However, these approaches are not suitable for OWOD, as unknown objects may be wrongly predicted as background due to the lack of specific supervision for distinguishing unknown objects from the background. The paper proposes a novel framework for Open World Object Detection called Open World Object Detection based on Non-Parametric classification (OWOD-NP). This method aims to address the challenges of identifying unknown objects and extending the knowledge base by incrementally introducing new object categories. OWOD-NP incorporates a non-parametric learni