Cross-modal embeddings are representations that combine information from different modalities, such as text, images, and audio, into a shared vector space. The goal is to create a unified representation that captures the relationships between different types of data. For example, in a cross-modal search system, you might search for images using a text description, or find relevant text based on an image. Cross-modal embeddings make this possible by aligning the features of text and images in the same embedding space.
These embeddings are typically learned using models that can handle multiple modalities simultaneously, such as CLIP (Contrastive Language-Image Pretraining) or VSE++ (Visual Semantic Embedding). These models learn to map text and images into a shared space where their relationships are preserved. This allows for tasks like image captioning, where an image is matched with a generated textual description, or visual question answering, where the model answers questions based on the content of an image.
Cross-modal embeddings are valuable because they enable the integration of information from different data sources, making it easier to perform tasks that involve multiple types of input. They support applications like multi-modal search engines, content-based recommendation systems, and multimodal analysis, where diverse data formats need to be understood and processed together.