  Learning from pseudo-labels that generated with VLMs~(Vision Language Models)
has been shown as a promising solution to assist open vocabulary detection
(OVD) in recent studies. However, due to the domain gap between VLM and
vision-detection tasks, pseudo-labels produced by the VLMs are prone to be
noisy, while the training design of the detector further amplifies the bias. In
this work, we investigate the root cause of VLMs' biased prediction under the
OVD context. Our observations lead to a simple yet effective paradigm, coded
MarvelOVD, that generates significantly better training targets and optimizes
the learning procedure in an online manner by marrying the capability of the
detector with the vision-language model. Our key insight is that the detector
itself can act as a strong auxiliary guidance to accommodate VLM's inability of
understanding both the ``background'' and the context of a proposal within the
image. Based on it, we greatly purify the noisy pseudo-labels via Online Mining
and propose Adaptive Reweighting to effectively suppress the biased training
boxes that are not well aligned with the target object. In addition, we also
identify a neglected ``base-novel-conflict'' problem and introduce stratified
label assignments to prevent it. Extensive experiments on COCO and LVIS
datasets demonstrate that our method outperforms the other state-of-the-arts by
significant margins. Codes are available at https://github.com/wkfdb/MarvelOVD
