Can attention enable MLPs to catch up with CNNs?

In the first week of May 2021, researchers from four different institutions: Google, Tsinghua University, Oxford University, and Facebook shared their latest work [1-4] on arXiv.org at almost the same time, each proposing new learning architectures, consisting mainly of linear layers, claiming them to be comparable or superior to convolutional-based models. This sparked immediate discussion and debate in both academic and industrial communities as to whether MLPs are sufficient, many thinking that learning architectures are returning to MLPs. Is this true? In the following, we give a brief history of learning architectures, including multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and transformers. We then examine what the four newly proposed architectures have in common. Finally, we give our views on challenges and directions for new learning architectures, hoping to inspire future research.