  Open-vocabulary detection (OVD) is a new object detection paradigm, aiming to
localize and recognize unseen objects defined by an unbounded vocabulary. This
is challenging since traditional detectors can only learn from pre-defined
categories and thus fail to detect and localize objects out of pre-defined
vocabulary. To handle the challenge, OVD leverages pre-trained cross-modal VLM,
such as CLIP, ALIGN, etc. Previous works mainly focus on the open vocabulary
classification part, with less attention on the localization part. We argue
that for a good OVD detector, both classification and localization should be
parallelly studied for the novel object categories. We show in this work that
improving localization as well as cross-modal classification complement each
other, and compose a good OVD detector jointly. We analyze three families of
OVD methods with different design emphases. We first propose a vanilla
method,i.e., cropping a bounding box obtained by a localizer and resizing it
into the CLIP. We next introduce another approach, which combines a standard
two-stage object detector with CLIP. A two-stage object detector includes a
visual backbone, a region proposal network (RPN), and a region of interest
(RoI) head. We decouple RPN and ROI head (DRR) and use RoIAlign to extract
meaningful features. In this case, it avoids resizing objects. To further
accelerate the training time and reduce the model parameters, we couple RPN and
ROI head (CRR) as the third approach. We conduct extensive experiments on these
three types of approaches in different settings. On the OVD-COCO benchmark, DRR
obtains the best performance and achieves 35.8 Novel AP$_{50}$, an absolute 2.8
gain over the previous state-of-the-art (SOTA). For OVD-LVIS, DRR surpasses the
previous SOTA by 1.9 AP$_{50}$ in rare categories. We also provide an object
detection dataset called PID and provide a baseline on PID.
