How does tokenization work in multimodal AI models, and why is it important?

Tokenization in multimodal AI models refers to the process of converting different forms of input (like text and images) into a format that the model can understand. For text, this typically involves splitting sentences into words or subwords, while for images, it may involve breaking down an image into pixels or using techniques like region proposal to identify objects. This process is crucial because it enables the model to generate embeddings for both text and visual data, allowing for effective integration and interpretation of multimodal inputs.