> We enable order with positional embeddings. For ViT, these positional embeddings are learned vectors with the same dimensionality as our patch embeddings.
> After creating the patch embeddings and prepending the “class” embedding, we sum them all with positional embeddings.
> These positional embeddings are learned during pretraining and (sometimes) during fine-tuning. During training, these embeddings converge into vector spaces where they show high similarity to their neighboring position embeddings — particularly those sharing the same column and row
Made no sense to me.
Like, okay, the positional embedding represents the position of a "patch" within the input image? What else? How are they used, what are they compared against, how are they fed into the neural network?