VLM
Vision Language Models (VLMs) are AI systems that integrate visual and textual data to understand and interpret images and text together.
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When to use it
Use Vision Language Models (VLMs) when a task requires simultaneous understanding of both visual and textual information, beyond what separate image and text models can achieve. VLMs combine these data types to perform complex tasks like generating captions for images or answering questions about visual content.
Quick example
In OpenAI's GPT-5.6 Sol, the model enhances object detection by integrating visual and textual data to achieve a 46.2 mAP@50 score. Sol is a VLM that processes both images and text inputs, allowing it to interpret and describe visual scenes with textual context. This integration makes Sol an instance of a VLM, demonstrating its capability to perform tasks such as generating image captions or visual question answering.
image/text input → VLM (Sol) → object detection/captioning → output
Ecosystem
VLMs interact with components that handle separate data modalities and integrate them for joint tasks. They often sit between image processing systems and language models, combining outputs for unified interpretation.
image processing → VLM → language model
Misconceptions
| Misconception | Rebuttal |
|---|---|
| VLMs are just image models | VLMs integrate both visual and textual data |
| Any model with images and text is a VLM | VLMs process and interpret both data types together |
Trade-offs
- Integrated understanding — requires more computational resources
- Richer context — increased complexity in model training and tuning
- Enhanced capabilities — potential for higher error rates due to data integration challenges