What are multimodal embeddings?

Summary slides

Why sharing a single vector space across modalities is the entire point

An embedding model trained only on text and a separate embedding model trained only on images might each produce excellent representations within their own modality, but their vectors live in entirely unrelated spaces with no meaningful relationship to each other, comparing a text vector from one model against an image vector from the other produces a meaningless result, since neither model was ever trained with any awareness that the other’s vector space exists. Multimodal embeddings solve this by training a single model to place both kinds of content into the same shared space from the start, so that similarity comparisons across modalities are actually meaningful rather than comparing two unrelated numbering systems that happen to look superficially similar.

This shared space is the specific technical achievement that makes cross-modal search and retrieval genuinely possible, and it connects directly to the broader discussion of multimodal models covered elsewhere in this collection, the same underlying principle, training a model to build connections between modalities from the start rather than bridging them after the fact, applies here specifically to the embedding and representation side of multimodal AI rather than the generative side.

How training actually produces this shared cross-modal space

Building a multimodal embedding model typically involves training on large amounts of paired data, images with their accompanying captions, video with its associated audio and transcripts, and adjusting the model so that paired content across modalities ends up positioned close together in the shared vector space, while unrelated content across modalities stays positioned far apart. Through exposure to enough of these pairs, the model learns to recognize the underlying conceptual relationships that connect a visual scene to its verbal description, letting it generalize this cross-modal understanding to new content it’s never encountered before, not just the specific pairs it was trained on.

This training approach is why the quality of a multimodal embedding model depends heavily on the quality and breadth of the paired data it was trained on, a model trained on a narrow, limited range of image-text pairs develops a correspondingly narrow understanding of how visual and verbal concepts relate, while a model trained on a broad, diverse range of paired content across many different domains and styles develops a more general, more broadly useful cross-modal understanding.

What this capability actually unlocks in practice

The most direct practical application of multimodal embeddings is cross-modal search, searching a large collection of images using a plain text description and finding the visually relevant results, or the reverse, finding text passages relevant to a given image. This has obvious value for content platforms with large media libraries, letting users search visual content the same intuitive way they’d search text, and it extends to recommendation systems that need to connect a user’s text-expressed preferences with visually similar products, and to content moderation systems that need to check whether an image matches concerning patterns described in text policy.

Beyond search, multimodal embeddings power retrieval-augmented generation systems that need to pull relevant visual content into a model’s context based on a text query, or the reverse, generating a text response informed by relevant images retrieved based on visual similarity to some reference image. This connects directly to the broader retrieval-augmented generation discussion covered elsewhere in this collection, extended here specifically to the case where the content being retrieved isn’t limited to text alone.

Why evaluating cross-modal quality specifically matters more than evaluating each modality separately

A multimodal embedding model can perform reasonably well at representing images among themselves and separately perform reasonably well at representing text among itself, while still performing poorly at the actual cross-modal task of connecting the two together, since strong within-modality representation doesn’t automatically guarantee strong cross-modal alignment between them. This mirrors the same evaluation nuance covered in this collection’s discussion of multimodal models more broadly, testing each modality in isolation misses exactly the cross-modal capability that’s the entire reason for using a multimodal embedding model in the first place.

A meaningful evaluation of a multimodal embedding model needs test cases specifically built around cross-modal retrieval, given a text query, does the model correctly surface the genuinely relevant image among a larger set of candidates, and given an image, does it correctly surface genuinely relevant text, rather than test cases that only ever compare content within a single modality where the model’s cross-modal alignment quality never actually gets exercised.

How multimodal embeddings interact with the dimension and similarity concerns covered elsewhere

Multimodal embeddings still carry the same dimension-count tradeoffs and rely on the same similarity measures, cosine similarity most commonly, covered in this collection’s dedicated articles on those topics, the multimodal aspect changes what’s being represented, not the underlying mathematical machinery used to compare vectors once they’re produced. This means the same considerations around dimension size, storage cost, and similarity threshold calibration apply just as directly to multimodal embeddings as they do to single-modality ones, with one added wrinkle, the similarity threshold that separates a genuinely relevant cross-modal match from an irrelevant one often needs its own separate calibration, since cross-modal similarity scores don’t necessarily follow the same distribution as within-modality similarity scores from the same model.

This is a detail worth checking explicitly rather than assuming, a threshold tuned for text-to-text similarity within a multimodal model may not transfer directly to text-to-image similarity using that same model, and a team building a cross-modal retrieval system benefits from validating threshold behavior specifically for the cross-modal comparison it actually needs, rather than assuming whatever threshold worked for a same-modality comparison automatically carries over.

The added complexity multimodal embeddings introduce to a pipeline

Building an embedding pipeline, covered in this collection’s dedicated article on that topic, around multimodal content introduces real additional complexity beyond a text-only pipeline, images and other non-text content typically require their own preprocessing steps, resizing, format normalization, extraction of relevant frames from video, before they can even be passed to the embedding model at all, and a pipeline handling multiple modalities needs to correctly route each content type through its appropriate preprocessing path while still producing vectors that land correctly in the same shared space.

This added complexity is a genuine cost that needs to be weighed against the actual value cross-modal capability provides for a specific application, a system that only ever needs to search text content gains nothing from the added complexity of a multimodal pipeline, while a system genuinely needing to connect visual and textual content benefits enough from that capability to justify the additional engineering investment it requires.

Common mistakes teams make around multimodal embeddings

1. Evaluating a multimodal embedding model’s quality using only within-modality test cases, missing whether it actually performs the cross-modal alignment that’s the entire point of using it.

2. Assuming a similarity threshold tuned for one modality pairing transfers directly to a different cross-modal comparison without separate validation.

3. Adopting multimodal embeddings for an application that only ever needs to search or compare content within a single modality, taking on unnecessary pipeline complexity for no corresponding benefit.

4. Training or selecting a multimodal embedding model based on narrow, limited paired training data, producing weak cross-modal understanding outside that narrow range.

5. Underestimating the preprocessing complexity non-text content adds to an embedding pipeline compared to handling text alone.

What connects these mistakes is underestimating how genuinely different cross-modal alignment is from single-modality representation quality, a multimodal embedding model’s real value lies specifically in the connections it builds between modalities, and evaluating, calibrating, and applying it well requires focusing directly on that cross-modal capability rather than assuming strong performance within each modality separately is enough on its own.

The deeper point about multimodal embeddings is that they extend the same foundational idea behind ordinary embeddings, turning meaning into a measurable, comparable numerical representation, across the boundary between different kinds of data entirely, and this capability is what lets AI systems finally treat an image and its description, or a video and its transcript, as genuinely connected pieces of the same underlying information rather than as separate, unrelated kinds of content that happen to describe the same thing.