  Recently, vision-language pre-training shows great potential in
open-vocabulary object detection, where detectors trained on base classes are
devised for detecting new classes. The class text embedding is firstly
generated by feeding prompts to the text encoder of a pre-trained
vision-language model. It is then used as the region classifier to supervise
the training of a detector. The key element that leads to the success of this
model is the proper prompt, which requires careful words tuning and ingenious
design. To avoid laborious prompt engineering, there are some prompt
representation learning methods being proposed for the image classification
task, which however can only be sub-optimal solutions when applied to the
detection task. In this paper, we introduce a novel method, detection prompt
(DetPro), to learn continuous prompt representations for open-vocabulary object
detection based on the pre-trained vision-language model. Different from the
previous classification-oriented methods, DetPro has two highlights: 1) a
background interpretation scheme to include the proposals in image background
into the prompt training; 2) a context grading scheme to separate proposals in
image foreground for tailored prompt training. We assemble DetPro with ViLD, a
recent state-of-the-art open-world object detector, and conduct experiments on
the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365
datasets. Experimental results show that our DetPro outperforms the baseline
ViLD in all settings, e.g., +3.4 APbox and +3.0 APmask improvements on the
novel classes of LVIS. Code and models are available at
https://github.com/dyabel/detpro.
