Multimodal embeddings refer to the representation of data that comes from multiple modalities, such as text, images, audio, and video, into a unified vector space. These embeddings combine information from different types of data to create a single representation that captures the relationships and correlations between them. For example, a multimodal embedding could represent an image and its associated textual description as a single vector, making it easier to compare or search for similar content across both modalities.
These embeddings are particularly useful in tasks that involve cross-modal interactions, such as image captioning, where the model needs to understand both the visual content of an image and the textual description of that image. Multimodal embeddings also support tasks like video analysis, where both visual and auditory features need to be integrated into a single representation for tasks like action recognition or sentiment analysis.
The goal of multimodal embeddings is to create a rich, shared representation that preserves the unique properties of each modality while enabling interactions between them. This allows models to handle more complex data relationships, making them applicable in fields like multimedia retrieval, recommendation systems, and autonomous systems that rely on multimodal inputs.