Definition
Multimodal AI systems integrate information from different sensory inputs to form a more comprehensive understanding of the world. Unlike unimodal models restricted to one type of data, multimodal models can correlate features across modalities, enabling capabilities like describing an image with text or answering questions based on both audio and visual cues. This leads to more human-like interaction and richer contextual awareness.
Summary
Refers to AI models capable of processing and generating content across multiple data types, such as text, images, audio, and video simultaneously.
Key Concepts
- Cross-modal Learning
- Data Fusion
- Representation Learning
- Sensory Input
Use Cases
- Visual question answering
- Video captioning
- Audio-visual speech recognition