The CLIP Encoder and Multimodal Bridges
Explore how the CLIP encoder builds a shared embedding space to connect text and images, allowing models to compare and relate different data modalities effectively. Understand the training process that aligns these embeddings, enabling applications like image retrieval, content filtering, and zero-shot classification without task-specific training.
Modern GenAI systems increasingly work with more than one type of data. Text, images, audio, and video all carry information in different forms, yet many real-world tasks require models to reason across these modalities. For example, a system might need to determine whether an image matches a caption, retrieve images based on a text query, or decide whether an image violates a content policy described in words.
To make this possible, models need a way to connect different modalities at the semantic level. One of the most influential approaches to doing this is CLIP (Contrastive Language–Image Pretraining). Rather than merging text and images directly, CLIP learns how to represent both in a shared space where they can be meaningfully compared.
Let’s learn how that bridge is built.
Multimodal learning
Text and images are fundamentally different kinds of data. Text is discrete and sequential, composed of tokens arranged in a specific order. Images, on the other hand, are continuous and spatial, represented as grids of pixel values. Because of these differences, the techniques used to process text and images are usually very different as well.
A language model processes sequences of tokens and learns relationships between words, phrases, and sentences. A vision model processes pixels and learns visual patterns such as edges, textures, objects, and scenes. These representations are not directly compatible. A sentence and an image cannot be compared in their raw forms.
This creates a core challenge for multimodal learning: “how can a model tell that a piece of text and an image refer to the same concept?”
The need for a common representation
To compare text and images, both must first be transformed into a format that supports comparison. In practice, this format is a vector embedding, which is a fixed-length numerical representation that captures semantic meaning.
The key idea behind CLIP-style models is simple but powerful: instead of directly merging text and image data, each modality is encoded separately into a vector, which is then placed in the same (shared) embedding space.
In this shared space:
Text descriptions and images that describe the same concept are close together.
Unrelated text and images are far apart.
Once both modalities live in the same space, matching becomes a geometric problem rather than a symbolic one. CLIP uses two separate encoders, one for text ...