My understanding is that early (and most extant) visual language models have a component module (called the image encoder) that transforms images into representations (called embeddings) the model's inner layers can process.

This is often a separate module grafted onto the main model, and further pre-trained (e.g. OpenAI's CLIP, SigLIP used in the Gemma 3 and PaliGemma series).

The image encoder approach has a few problems.

One problem is that many like Gemma 3's encoder have fixed image resolution constraints and inputs must be resized with all the attendant distortions that causes with spatial understanding. However, the Gemma 4 series image encoders overcame this and can handle variable-dimension inputs.

Two, these image encoders are somewhat large (ranging from 300-500M parameters) requiring extra memory and FLOPs to run.

Three, say we need to fine-tune a vision language model, updates to its weights, may affect its understanding of the representations generated by the image encoder if we don't fine-tune both together.

The new Gemma-4-12B replaces the encoder (with its many attention layers and large parameter count) with a simple linear projection to generate the embeddings for images. That reduces the computational requirements and simplifies the input pipelines for image processing.

I don't have any expertise on the topic though and might very well be wrong on some details.