A multimodal model is an AI system capable of processing and understanding data from multiple modalities, such as text, images, audio, and video. Unlike unimodal models that handle one data type, multimodal models integrate information across different formats to deliver richer and more accurate results.
These models often use shared representations to link modalities. For instance, in CLIP (Contrastive Language–Image Pretraining), the model learns to align images with their corresponding text descriptions, enabling tasks like image captioning and visual search.
Applications of multimodal models include multimedia search engines, virtual assistants, and healthcare diagnostics. For example, a model might analyze both medical images and patient history (text) to assist in diagnosis. In e-commerce, multimodal systems enhance product recommendations by considering both product images and user reviews.
Training multimodal models requires diverse datasets containing paired data, such as images with captions or videos with transcripts. Popular architectures, such as transformers, are adapted to handle inputs from different modalities by using modality-specific encoders and shared embeddings.
Multimodal models are pivotal for next-generation AI systems, making interactions more intuitive and human-like. However, challenges like aligning data from diverse modalities and ensuring scalability remain areas of active research.