  Text prompts are crucial for generalizing pre-trained open-set object
detection models to new categories. However, current methods for text prompts
are limited as they require manual feedback when generalizing to new
categories, which restricts their ability to model complex scenes, often
leading to incorrect detection results. To address this limitation, we propose
a novel visual prompt method that learns new category knowledge from a few
labeled images, which generalizes the pre-trained detection model to the new
category. To allow visual prompts to represent new categories adequately, we
propose a statistical-based prompt construction module that is not limited by
predefined vocabulary lengths, thus allowing more vectors to be used when
representing categories. We further utilize the category dictionaries in the
pre-training dataset to design task-specific similarity dictionaries, which
make visual prompts more discriminative. We evaluate the method on the ODinW
dataset and show that it outperforms existing prompt learning methods and
performs more consistently in combinatorial inference.
