Open World Object Detection (OWOD) is a challenging and realistic task that
extends beyond the scope of standard Object Detection task. It involves
detecting both known and unknown objects while integrating learned knowledge
for future tasks. However, the level of 'unknownness' varies significantly
depending on the context. For example, a tree is typically considered part of
the background in a self-driving scene, but it may be significant in a
household context. We argue that this external or contextual information should
already be embedded within the known classes. In other words, there should be a
semantic or latent structure relationship between the known and unknown items
to be discovered. Motivated by this observation, we propose Hyp-OW, a method
that learns and models hierarchical representation of known items through a
SuperClass Regularizer. Leveraging this learned representation allows us to
effectively detect unknown objects using a Similarity Distance-based Relabeling
module. Extensive experiments on benchmark datasets demonstrate the
effectiveness of Hyp-OW achieving improvement in both known and unknown
detection (up to 6 points). These findings are particularly pronounced in our
newly designed benchmark, where a strong hierarchical structure exists between
known and unknown objects.

介绍了一种新的 Open World Object Detection 模型 ——Hyp-OW，它使用超类正则化器学习已知物品的层次表征，并通过相似度距离重新调整模块来有效检测未知物体，实验证明，该模型在已知和未知检测方面都具有很高的精度，特别是在已知和未知对象之间存在强层次结构的情况下。