What types of data are used to train Vision-Language Models?

Vision-Language Models (VLMs) are trained using diverse datasets that combine visual and textual information to enable understanding and generation across both modalities. The primary types of data used fall into three broad categories: paired image-text data, structured datasets with annotations, and web-scale multimodal content. Below is a detailed breakdown:

1. Paired Image-Text Data This is the core training material for VLMs. It consists of images paired with descriptive text, such as captions or labels. For example, datasets like COCO (Common Objects in Context) provide images with multiple captions describing objects, actions, and contexts[7]. Similarly, Flickr30k contains user-uploaded photos with human-generated captions. These datasets help models learn associations between visual elements (e.g., a dog running) and their textual descriptions. Some models also use synthetic data, where images are programmatically combined with text (e.g., rendered scenes with overlayed labels).

2. Structured Annotation Datasets These datasets include fine-grained annotations beyond simple captions, such as object bounding boxes, segmentation masks, or attribute labels. For instance, Visual Genome links images to detailed scene graphs that describe objects, relationships, and attributes[7]. Models trained on such data can better understand spatial relationships (e.g., “a cat sitting on a chair”) or compositional semantics (e.g., “a red car next to a tree”). Medical VLMs might use annotated X-rays paired with diagnostic reports to learn domain-specific correlations.

3. Web-Scale Multimodal Content Large-scale scraped data from the internet—such as social media posts, web pages with embedded images, and video subtitles—provides noisy but diverse training material. Tools like CLIP use hundreds of millions of image-text pairs from public websites to learn broad visual-textual alignments[7]. While this data is less curated, its sheer volume helps models generalize to open-world scenarios. However, it requires preprocessing to filter irrelevant or low-quality content.

Developers should note that data diversity and balance are critical. For example, overrepresenting specific object categories (e.g., “cars” in automotive datasets) can bias model outputs. Additionally, preprocessing steps like tokenization (for text) and normalization (for images) ensure consistency across modalities. By combining these data types, VLMs achieve robust cross-modal reasoning, enabling applications like visual question answering or automated content moderation.