Review - Perceiver: General Perception with Iterative Attention

The paper "Perceiver: General Perception with Iterative Attention" presents a novel approach to training a single model on various types of data such as image, audio, video, and point cloud. It builds upon the Transformer architecture and utilizes an asymmetric attention mechanism to scale transformers for high-dimensional audio/visual data. The authors introduce a latent bottleneck that allows the model to handle hundreds of thousands of inputs while keeping the number of parameters minimal. This technique results in a total complexity of O(MN + LN²), where M is the dimensionality of the input, N is the dimensionality of the latent array, and L is the depth of the transformer. The paper represents a significant step towards general-purpose models that can easily scale to any data type. Future works may build upon these ideas to further push the limits of model-based generalizability.