CLIM: Contrastive Language-Image Mosaic for Region Representation

• URL: http://arxiv.org/abs/2312.11376v2
• Date: Tue, 19 Dec 2023 05:08:45 GMT
• Title: CLIM: Contrastive Language-Image Mosaic for Region Representation
• Authors: Size Wu, Wenwei Zhang, Lumin Xu, Sheng Jin, Wentao Liu, Chen Change Loy
• Abstract summary: Contrastive Language-Image Mosaic (CLIM) is a novel approach for aligning region and text representations. CLIM consistently improves different open-vocabulary object detection methods. It can effectively enhance the region representation of vision-language models.
• Score: 58.05870131126816
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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