What is Perceiver IO and how does it handle arbitrary input/output modalities in vision systems?

Perceiver IO uses a latent bottleneck and cross-attention to process arbitrary-sized inputs and outputs efficiently.

Perceiver IO is designed to overcome the quadratic scaling of transformers on large inputs like high-resolution images or multimodal data. It introduces a fixed-size latent array that repeatedly cross-attends to the input, regardless of input size. This latent space becomes a compressed representation of the entire input. Outputs are generated by attending from task-specific queries to the latent array, making the architecture flexible for classification, segmentation, and multimodal tasks.