  Open-vocabulary object detection (OVD) aims to scale up vocabulary size to
detect objects of novel categories beyond the training vocabulary. Recent work
resorts to the rich knowledge in pre-trained vision-language models. However,
existing methods are ineffective in proposal-level vision-language alignment.
Meanwhile, the models usually suffer from confidence bias toward base
categories and perform worse on novel ones. To overcome the challenges, we
present MEDet, a novel and effective OVD framework with proposal mining and
prediction equalization. First, we design an online proposal mining to refine
the inherited vision-semantic knowledge from coarse to fine, allowing for
proposal-level detection-oriented feature alignment. Second, based on causal
inference theory, we introduce a class-wise backdoor adjustment to reinforce
the predictions on novel categories to improve the overall OVD performance.
Extensive experiments on COCO and LVIS benchmarks verify the superiority of
MEDet over the competing approaches in detecting objects of novel categories,
e.g., 32.6% AP50 on COCO and 22.4% mask mAP on LVIS.
