  Open vocabulary object detection (OVD) aims at seeking an optimal object
detector capable of recognizing objects from both base and novel categories.
Recent advances leverage knowledge distillation to transfer insightful
knowledge from pre-trained large-scale vision-language models to the task of
object detection, significantly generalizing the powerful capabilities of the
detector to identify more unknown object categories. However, these methods
face significant challenges in background interpretation and model overfitting
and thus often result in the loss of crucial background knowledge, giving rise
to sub-optimal inference performance of the detector. To mitigate these issues,
we present a novel OVD framework termed LBP to propose learning background
prompts to harness explored implicit background knowledge, thus enhancing the
detection performance w.r.t. base and novel categories. Specifically, we devise
three modules: Background Category-specific Prompt, Background Object
Discovery, and Inference Probability Rectification, to empower the detector to
discover, represent, and leverage implicit object knowledge explored from
background proposals. Evaluation on two benchmark datasets, OV-COCO and
OV-LVIS, demonstrates the superiority of our proposed method over existing
state-of-the-art approaches in handling the OVD tasks.
