Multimodal information combines data from multiple modalities, such as text, images, audio, and video, to enable richer and more accurate AI applications. By integrating diverse data types, systems can provide a deeper understanding of context and improve decision-making.
For example, in multimedia search, a user may upload an image and type a text query to refine search results. The system processes the image’s visual features and the text’s semantic meaning to find the most relevant matches. Similarly, in autonomous driving, multimodal information from cameras, LiDAR sensors, and GPS data ensures robust navigation by combining visual, spatial, and location-based inputs.
Multimodal data is also used in recommendation systems. For instance, a product recommendation engine might analyze a user’s browsing history (text) alongside product images to suggest items that match both their preferences and visual interests.
Advanced AI models, such as CLIP (Contrastive Language–Image Pretraining), leverage multimodal training to link text and images, enabling tasks like generating captions for images or finding related visuals from text descriptions.
Multimodal information is key to applications in healthcare, education, and e-commerce, where combining various data sources enhances user experiences and ensures more reliable outcomes.