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Multimodal
Vision-Language Model
Definition
Vision-language models (VLMs) are AI systems that understand both images and text together, enabling tasks like image captioning, visual Q&A, and document understanding.
Why It Matters
Most real-world information combines text and images. Vision-language models enable AI to understand charts, analyze product photos, read documents with complex layouts, and answer questions about visual content. This dramatically expands what AI applications can process.
Key Models
- GPT-4o: Native multimodal, high capability
- Claude 3.5 Sonnet: Strong document understanding
- Gemini 2.0: Google’s multimodal flagship
- LLaVA: Open-source VLM
- Qwen-VL: Alibaba’s vision-language model
Capabilities
- Image Captioning: Describe what’s in an image
- Visual Q&A: Answer questions about images
- Document Understanding: Extract info from PDFs, charts
- OCR+Understanding: Read and reason about text in images
- Object Detection: Identify and locate objects
- Visual Reasoning: Multi-step reasoning about visual content
Applications
Document processing, product image analysis, accessibility (describing images), visual search, automated data extraction from charts, and quality inspection.