The goal of this paper is open-vocabulary object detection (OVOD) $\unicode{x2013}$ building a model that can detect objects beyond the set of categories seen at training, thus enabling the user to specify categories of interest at inference without the need for model retraining. We adopt a standard two-stage object detector architecture, and explore three ways for specifying novel categories: via language descriptions, via image exemplars, or via a combination of the two. We make three contributions: first, we prompt a large language model (LLM) to generate informative language descriptions for object classes, and construct powerful text-based classifiers; second, we employ a visual aggregator on image exemplars that can ingest any number of images as input, forming vision-based classifiers; and third, we provide a simple method to fuse information from language descriptions and image exemplars, yielding a multi-modal classifier. When evaluating on the challenging LVIS open-vocabulary benchmark we demonstrate that: (i) our text-based classifiers outperform all previous OVOD works; (ii) our vision-based classifiers perform as well as text-based classifiers in prior work; (iii) using multi-modal classifiers perform better than either modality alone; and finally, (iv) our text-based and multi-modal classifiers yield better performance than a fully-supervised detector.

翻译：本文的目标是实现开放词汇目标检测（OVOD）——构建一个能够检测训练阶段未见类别集合中物体的模型，从而使用户在推理时无需重新训练模型即可指定感兴趣的类别。我们采用标准的两阶段目标检测器架构，并探索三种指定新类别的方式：通过语言描述、通过图像示例或通过两者的结合。我们做出三项贡献：首先，我们利用大型语言模型（LLM）生成物体类别的信息性语言描述，并构建强大的基于文本的分类器；其次，我们采用视觉聚合器处理图像示例，该聚合器能够以任意数量的图像作为输入，形成基于视觉的分类器；第三，我们提供一种简单的方法来融合语言描述和图像示例的信息，从而得到多模态分类器。在具有挑战性的LVIS开放词汇基准上的评估表明：（i）我们的基于文本的分类器优于所有先前的OVOD工作；（ii）我们的基于视觉的分类器性能与先前工作中的基于文本的分类器相当；（iii）使用多模态分类器的性能优于单独使用任一模态；（iv）我们的基于文本和多模态分类器在性能上优于全监督检测器。