Embeddings handle multimodal data (data from different sources or modalities like text, images, and audio) with high variance by learning shared representations that capture common features across modalities. For example, in a cross-modal setting, embeddings can be trained to map text and images into a unified vector space where similarities between modalities are preserved. This allows the model to handle different data types that may vary widely in format.
To manage high variance, models that handle multimodal data often use specialized architectures, such as multi-stream neural networks or transformers, that process each modality separately before combining the learned representations. These models are trained to learn meaningful relationships between the different data types, ensuring that the embedding space captures both the individual characteristics of each modality and their interactions.
However, high variance across modalities can introduce challenges, such as the difficulty in aligning data points from different sources. Techniques like normalization and attention mechanisms help address these challenges by focusing on the most relevant features across the modalities. Ultimately, multimodal embeddings enable the model to integrate heterogeneous data into a single framework that can handle complex, real-world tasks like visual question answering or image captioning.