I feel like I have a schematic understanding of how transformer models process text. But I struggle to understand how the same concept could be used with images or other less linear data types. I understand that such data can be vectorized so it’s represented as a point or series of points in a higher dimensional space, but how does such a process capture the essential high level perceptual aspects well enough to be able to replicate large scale features? Or equivalently, how are the dimensions chosen? I have to imagine that simply looking at an image as an RGB pixel sequence would largely miss the point of what it “is” and represents.