  Open-vocabulary object detection aims to detect novel object categories
beyond the training set.
  The advanced open-vocabulary two-stage detectors employ instance-level
visual-to-visual knowledge distillation to align the visual space of the
detector with the semantic space of the Pre-trained Visual-Language Model
(PVLM).
  However, in the more efficient one-stage detector, the absence of
class-agnostic object proposals hinders the knowledge distillation on unseen
objects, leading to severe performance degradation.
  In this paper, we propose a hierarchical visual-language knowledge
distillation method, i.e., HierKD, for open-vocabulary one-stage detection.
  Specifically, a global-level knowledge distillation is explored to transfer
the knowledge of unseen categories from the PVLM to the detector.
  Moreover, we combine the proposed global-level knowledge distillation and the
common instance-level knowledge distillation to learn the knowledge of seen and
unseen categories simultaneously.
  Extensive experiments on MS-COCO show that our method significantly surpasses
the previous best one-stage detector with 11.9\% and 6.7\% $AP_{50}$ gains
under the zero-shot detection and generalized zero-shot detection settings, and
reduces the $AP_{50}$ performance gap from 14\% to 7.3\% compared to the best
two-stage detector.
