This paper addresses the challenging problem of open-vocabulary object
detection (OVOD) where an object detector must identify both seen and unseen
classes in test images without labeled examples of the unseen classes in
training. A typical approach for OVOD is to use joint text-image embeddings of
CLIP to assign box proposals to their closest text label. However, this method
has a critical issue: many low-quality boxes, such as over- and
under-covered-object boxes, have the same similarity score as high-quality
boxes since CLIP is not trained on exact object location information. To
address this issue, we propose a novel method, LP-OVOD, that discards
low-quality boxes by training a sigmoid linear classifier on pseudo labels
retrieved from the top relevant region proposals to the novel text.
Experimental results on COCO affirm the superior performance of our approach
over the state of the art, achieving $\textbf{40.5}$ in $\text{AP}_{novel}$
using ResNet50 as the backbone and without external datasets or knowing novel
classes during training. Our code will be available at
this https URL

本文提出了一种解决开放词汇物体检测（OVOD）问题的方法，该方法使用共同的文本图像嵌入来为边界提案分配最接近的文本标签，并通过从前 n 个相关区域提案中检索伪标签来训练一个分类器以丢弃低质量的边界框，实验证明我们的方法在 COCO 数据集上比现有方法表现优秀，AP_novel 达到了 40.5。