A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their annotations progressively arrive. Previous works rely on independent modules to recognize unknown categories and perform incremental learning, respectively. In this paper, we provide a unified perspective: Semantic Topology. During the life-long learning of an open-world object detector, all object instances from the same category are assigned to their corresponding pre-defined node in the semantic topology, including the `unknown' category. This constraint builds up discriminative feature representations and consistent relationships among objects, thus enabling the detector to distinguish unknown objects out of the known categories, as well as making learned features of known objects undistorted when learning new categories incrementally. Extensive experiments demonstrate that semantic topology, either randomly-generated or derived from a well-trained language model, could outperform the current state-of-the-art open-world object detectors by a large margin, e.g., the absolute open-set error is reduced from 7832 to 2546, exhibiting the inherent superiority of semantic topology on open-world object detection.
翻译:更现实的天体探测模式( Open- World 物体探测), 最近在社区中引起了越来越多的研究兴趣。 合格的开放世界天体探测器不仅可以识别已知类别的物体,还可以发现未知的物体, 并逐渐在它们的注释逐渐到达时逐渐学会对其进行分类。 以前的作品依靠独立模块识别未知的类别, 并进行渐进学习。 在本文中, 我们提供了一个统一的观点: 语义地形学。 在开放世界天体探测器的终身学习中, 同一类别的所有物体都被指定在语义表层中相应的预设节点, 包括“ 未知” 类别。 这一限制增加了歧视性特征的表达方式, 以及不同对象之间的一致性, 从而使得探测器能够区分已知类别的未知对象, 以及使学习新类别时不扭曲的已知物体的学习特征。 广泛的实验表明, 语义表层学, 无论是随机生成的还是从受过良好训练的语言模型衍生的, 都可能超越当前状态的开放世界天体物体探测器,, 包括“ 未知” 类别 。