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

To train vision-language models (VLMs), three primary types of data are required: paired image-text data, diverse and large-scale datasets, and structured annotations or metadata. Each plays a distinct role in enabling the model to understand relationships between visual and textual information, generalize across tasks, and perform accurately in real-world scenarios.

First, paired image-text data is the foundational requirement. This consists of images directly linked to textual descriptions, such as captions, labels, or contextual information. For example, datasets like COCO (Common Objects in Context) provide images with detailed captions and object annotations, while web-scraped datasets like LAION-5B use alt-text descriptions from publicly available images. These pairs teach the model to align visual features (e.g., objects, scenes) with corresponding words or phrases. Without this alignment, VLMs cannot learn to generate relevant text from images or retrieve images based on text queries. Even noisy or imperfect pairings (e.g., social media images with hashtags) can be useful, provided there’s enough volume to mitigate inconsistencies.

Second, diversity and scale are critical. VLMs must handle a wide range of visual concepts, languages, and contexts, which requires datasets covering multiple domains (e.g., nature, urban environments), languages beyond English, and varied lighting or object configurations. For instance, a medical VLM might need X-ray images paired with diagnostic reports, while a retail-focused model could use product images with multilingual descriptions. Large-scale datasets (e.g., LAION-5B with 5 billion image-text pairs) help models generalize better, but balancing quantity with quality is key. Web-scraped data often includes irrelevant or biased samples, so preprocessing steps like filtering explicit content or deduplication are necessary to improve usability.

Finally, structured annotations or metadata enhance model performance for specific tasks. While raw image-text pairs suffice for basic alignment, tasks like object detection or visual question answering require additional labels. For example, bounding boxes in COCO or Flickr30K enable models to localize objects within images, while metadata like timestamps or geolocation (e.g., in satellite imagery datasets) can provide contextual clues. During fine-tuning, smaller datasets with task-specific annotations (e.g., labeled regions in medical scans) are often used to adapt pre-trained VLMs to specialized use cases. Structured data reduces ambiguity and helps the model learn precise relationships between visual elements and text.