  Detecting objects accurately from a large or open vocabulary necessitates the
vision-language alignment on region representations. However, learning such a
region-text alignment by obtaining high-quality box annotations with text
labels or descriptions is expensive and infeasible. In contrast, collecting
image-text pairs is simpler but lacks precise object location information to
associate regions with texts. In this paper, we propose a novel approach called
Contrastive Language-Image Mosaic (CLIM), which leverages large-scale
image-text pairs effectively for aligning region and text representations. CLIM
combines multiple images into a mosaicked image and treats each image as a
`pseudo region'. The feature of each pseudo region is extracted and trained to
be similar to the corresponding text embedding while dissimilar from others by
a contrastive loss, enabling the model to learn the region-text alignment
without costly box annotations. As a generally applicable approach, CLIM
consistently improves different open-vocabulary object detection methods that
use caption supervision. Furthermore, CLIM can effectively enhance the region
representation of vision-language models, thus providing stronger backbones for
open-vocabulary object detectors. Our experimental results demonstrate that
CLIM improves different baseline open-vocabulary object detectors by a large
margin on both OV-COCO and OV-LVIS benchmarks. The code is available at
https://github.com/wusize/CLIM.
