Vision Language Models

Vision Language Models are multimodal neural networks that jointly process images (or video frames) and text to understand, reason, and generate cross-modal outputs. They pair a vision encoder—CNN, Vision Transformer, or CLIP image backbone—with a language model and fuse their embeddings through cross-attention layers or a projection bridge. Trained on image–caption pairs or web alt-text, these models answer visual questions, create detailed captions, locate objects by text prompts, and ground large-language-model (LLM) responses in pixel data. Flagship examples include OpenAI GPT-4o, Google Gemini, and Meta LLaVA-Next. Metrics such as VQA accuracy, CIDEr, and grounding recall gauge performance. Applications span accessibility (image alt-text), e-commerce search (“show shoes like this”), robotics perception, and Retrieval-Augmented Generation where screenshots or diagrams enrich context. Challenges—dataset bias, hallucinated objects, and high GPU memory—are tackled with synthetic data, region-level supervision, and efficient adapters.