Yes, embeddings can be used for multimodal data, which refers to data that comes from different modalities or sources, such as text, images, audio, and video. Multimodal embeddings integrate these different types of data into a shared vector space, allowing models to process and make predictions based on data from multiple modalities simultaneously.
For example, in a multimodal search system, a user might search for an image using a text query. In this case, both the image and the text are represented as embeddings in the same vector space, enabling the model to find relevant images based on their semantic content rather than just pixel similarities.
Advances in models like CLIP and ALIGN, which learn joint embeddings for text and images, have significantly improved multimodal learning. These models enable cross-modal understanding, where information from one modality (like text) can be used to enhance or guide the interpretation of another modality (like images). This opens up many possibilities in fields like healthcare (combining medical text and images) and robotics (integrating sensor data with visual information).