Definition
A multi-modal capability where an AI agent or RAG pipeline processes visual inputs—such as images or video frames—alongside a natural language query to generate a grounded textual response. In an agentic context, it enables the model to reason over non-textual data by integrating Vision-Language Models (VLMs) to interpret spatial relationships and semantic content within the retrieved context.
Distinguished from OCR; VQA interprets the 'meaning' and 'context' of visual elements rather than just transcribing text characters.
"A forensic analyst examining a high-resolution photograph to answer specific questions from a detective who isn't at the scene."
- Multimodal RAG(Implementation Framework)
- Vision-Language Model (VLM)(Core Component)
- Vector Embeddings (Multi-modal)(Prerequisite)
- Zero-shot Image Classification(Sub-task)
Conceptual Overview
A multi-modal capability where an AI agent or RAG pipeline processes visual inputs—such as images or video frames—alongside a natural language query to generate a grounded textual response. In an agentic context, it enables the model to reason over non-textual data by integrating Vision-Language Models (VLMs) to interpret spatial relationships and semantic content within the retrieved context.
Disambiguation
Distinguished from OCR; VQA interprets the 'meaning' and 'context' of visual elements rather than just transcribing text characters.
Visual Analog
A forensic analyst examining a high-resolution photograph to answer specific questions from a detective who isn't at the scene.