Vision-Language Model (VLM)

A vision-language model (VLM) is an AI model that can process and reason about both images and text simultaneously. When you upload a photo to Claude or ChatGPT and ask questions about it, you are using a VLM.

What VLMs can do

• Image understanding: Describe what is in a photo, identify objects, read text, and interpret scenes.
• Visual question answering: Answer specific questions about images ("How many people are in this photo?" "What brand is shown on the sign?").
• Document analysis: Read and interpret complex documents with mixed text, tables, charts, and images.
• Chart and graph interpretation: Extract data and insights from visualisations.
• Visual reasoning: Make inferences that require combining visual and textual information.
• Image comparison: Identify differences or similarities between multiple images.

How VLMs work

VLMs typically combine a vision encoder with a language model:

1. Vision encoder: Processes the image, breaking it into patches and converting each into a vector representation. Often based on a Vision Transformer (ViT) architecture.
2. Alignment layer: Translates the visual representations into a format the language model can understand. This is where the "bridge" between vision and language exists.
3. Language model: Processes the combined visual and textual information to generate responses.

Some newer architectures process images and text natively within a single unified model rather than combining separate components.

Key VLMs

• Claude (Anthropic): Strong document and chart analysis, precise visual reasoning.
• GPT-4o (OpenAI): Broad visual capabilities, integrated with DALL-E for generation.
• Gemini (Google): Designed as multimodal from the ground up, handles video alongside images.
• LLaVA: Open-source VLM that pioneered many visual instruction-following techniques.
• Qwen-VL: Strong open-source option with multilingual visual understanding.

Business applications

• Invoice and receipt processing: Extract structured data from photographed or scanned financial documents.
• Quality inspection: Analyse product images for defects or compliance.
• Retail analytics: Understand shelf layouts, signage, and store conditions from photos.
• Accessibility: Generate descriptions of images for visually impaired users.
• Insurance: Assess damage from photos submitted with claims.
• Real estate: Analyse property photos for listing descriptions and valuations.

Limitations

• VLMs can hallucinate visual details — reporting objects or text that is not actually in the image.
• Small text, low-resolution images, and complex spatial relationships remain challenging.
• Performance varies significantly across different types of visual content.