Meet SparseFormer: A Neural Architecture for Sparse Visual Recognition with Limited Tokens

Convolutional neural networks (CNNs) construct features by applying convolutional filters to each unit of pictures or feature maps. A lightweight early convolution module in the SparseFormer pulls image features from a given picture. It learns to represent a picture via latent transformers and a very small number of tokens.