Joint embeddings combine data from multiple modalities (like text, image, and audio) into a shared vector space. The process involves learning embeddings for each modality and then aligning them into a common feature space, where similar data across modalities are represented by similar vectors. For example, in a joint embedding for image-text data, an image of a dog and its caption “a dog running” would have similar vector representations, allowing the model to understand their relationship.
Joint embeddings are typically learned using techniques like contrastive learning or cross-modal attention mechanisms. In contrastive learning, the model is trained to bring similar data points closer together in the embedding space while pushing dissimilar ones farther apart. For instance, the model might be trained to make sure an image of a car and the word “car” have similar representations, while an image of a tree and the word “car” are distant in the vector space.
These joint embeddings enable tasks such as image retrieval using textual queries, or vice versa, by allowing systems to compare and contrast data from different modalities in a shared space. The ability to process and relate data from multiple sources enhances the model’s understanding and makes it possible to leverage multimodal data in applications like caption generation, cross-modal search, and multimodal recommendation systems.