Deriving reliable region-word alignment from image-text pairs is critical to
learn object-level vision-language representations for open-vocabulary object
detection. Existing methods typically rely on pre-trained or self-trained
vision-language models for alignment, which are prone to limitations in
localization accuracy or generalization capabilities. In this paper, we propose
CoDet, a novel approach that overcomes the reliance on pre-aligned
vision-language space by reformulating region-word alignment as a co-occurring
object discovery problem. Intuitively, by grouping images that mention a shared
concept in their captions, objects corresponding to the shared concept shall
exhibit high co-occurrence among the group. CoDet then leverages visual
similarities to discover the co-occurring objects and align them with the
shared concept. Extensive experiments demonstrate that CoDet has superior
performances and compelling scalability in open-vocabulary detection, e.g., by
scaling up the visual backbone, CoDet achieves 37.0 $\text{AP}^m_{novel}$ and
44.7 $\text{AP}^m_{all}$ on OV-LVIS, surpassing the previous SoTA by 4.2
$\text{AP}^m_{novel}$ and 9.8 $\text{AP}^m_{all}$. Code is available at
this https URL

利用共现对象发现的方法，CoDet 克服了对预对齐视觉 - 语言空间依赖的限制，通过图像标题中提及共享概念的图像分组，发现并与共享概念对齐共现对象，从而实现了对象级别的视觉 - 语言表示，具有卓越的性能和可扩展性。