MLP is basically the vanilla neural network. The thing everyone sees first when getting taught about deep learning or similar. It turned out that (usually) MLPs

MLP is basically the vanilla neural network. The thing everyone sees first when getting taught about deep learning or similar. It turned out that (usually) MLPs are not very efficient to process matrix structures with it (e.g. 2d matrices like images). So, computer vision invented convolutional neural networks specifically to make image data highly efficient to process for neural networks. They outperformed MLPs in basically all aspects across the board. Transformers / Attention is a relatively new invention initially made to solve NLP problems more efficiently; but as it turns out, they work great on images as well. TLDR - we've come full circle.