Token Mixer

Token mixers are the core components within vision transformers and related architectures responsible for aggregating information across image features (tokens). Current research focuses on developing efficient and effective token mixers, exploring alternatives to computationally expensive self-attention mechanisms, such as convolutional operations, MLPs, and frequency-based methods, with architectures like MetaFormer providing a general framework for evaluating various mixer designs. These advancements aim to improve the speed and efficiency of vision models while maintaining or improving accuracy, impacting both resource-constrained applications (e.g., mobile devices) and large-scale image processing tasks.