  Open-vocabulary object detection aims to provide object detectors trained on
a fixed set of object categories with the generalizability to detect objects
described by arbitrary text queries. Previous methods adopt knowledge
distillation to extract knowledge from Pretrained Vision-and-Language Models
(PVLMs) and transfer it to detectors. However, due to the non-adaptive proposal
cropping and single-level feature mimicking processes, they suffer from
information destruction during knowledge extraction and inefficient knowledge
transfer. To remedy these limitations, we propose an Object-Aware Distillation
Pyramid (OADP) framework, including an Object-Aware Knowledge Extraction (OAKE)
module and a Distillation Pyramid (DP) mechanism. When extracting object
knowledge from PVLMs, the former adaptively transforms object proposals and
adopts object-aware mask attention to obtain precise and complete knowledge of
objects. The latter introduces global and block distillation for more
comprehensive knowledge transfer to compensate for the missing relation
information in object distillation. Extensive experiments show that our method
achieves significant improvement compared to current methods. Especially on the
MS-COCO dataset, our OADP framework reaches $35.6$ mAP$^{\text{N}}_{50}$,
surpassing the current state-of-the-art method by $3.3$ mAP$^{\text{N}}_{50}$.
Code is released at https://github.com/LutingWang/OADP.
