  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.
