>It's amazing to me how people will confidently spout utter nonsense.
Ok.
You seem to be conflating some things, evident when you suddenly dropped the ViT paper as evidentiary. During the analysis of images, tiles and transformers (such as a ViT) are used. This is the model of processing the image to obtain useful information, such as to do OCR (you might notice that that word used repeatedly in the Google paper).
But to actually use the image, context has to be drawn from it. This is pretty bog standard OCR, object detection and classification, sentiment analysis, etc. This yields tokens.
Have you called the API and generated tokens from an image yet? Try it. You'll find they aren't as magical and mysterious as you believe, and your quasi-understanding of a ViT is not relevant to the tokens retrieved from a multimodal LLM.
There is the notion of semantic image tokens, which is an inner property of the analysis engine for images (and, conversely, the generation engine) but it is not what we're talking about. If an image was somehow collapsed into a 16x16 array of integers and amazingly it could still tell you the words on books and the objects that appear, that would be amazing. Too amazing.
>But to actually use the image, context has to be drawn from it. This is pretty bog standard OCR, object detection and classification, sentiment analysis, etc. This yields tokens
None of that is necessary for an Autoregressive Transformer. You can train the transformer to predict text tokens given interleaved image and text input tokens in the context window.
Google have already told us how this works. Read the Flamingo or Pali papers. You are wrong. Very wrong.
It's incredible that people will crucify LLMs for "hallucinating" but then there are humans like you running around.
Ok.
You seem to be conflating some things, evident when you suddenly dropped the ViT paper as evidentiary. During the analysis of images, tiles and transformers (such as a ViT) are used. This is the model of processing the image to obtain useful information, such as to do OCR (you might notice that that word used repeatedly in the Google paper).
But to actually use the image, context has to be drawn from it. This is pretty bog standard OCR, object detection and classification, sentiment analysis, etc. This yields tokens.
Have you called the API and generated tokens from an image yet? Try it. You'll find they aren't as magical and mysterious as you believe, and your quasi-understanding of a ViT is not relevant to the tokens retrieved from a multimodal LLM.
There is the notion of semantic image tokens, which is an inner property of the analysis engine for images (and, conversely, the generation engine) but it is not what we're talking about. If an image was somehow collapsed into a 16x16 array of integers and amazingly it could still tell you the words on books and the objects that appear, that would be amazing. Too amazing.