A central goal of machine learning is the development of systems that can
solve many problems in as many data domains as possible. Current architectures,
however, cannot be applied beyond a small set of stereotyped settings, as they
bake in domain & task assumptions or scale poorly to large inputs or outputs.
In this work, we propose Perceiver IO, a general-purpose architecture that
handles data from arbitrary settings while scaling linearly with the size of
inputs and outputs. Our model augments the Perceiver with a flexible querying
mechanism that enables outputs of various sizes and semantics, doing away with
the need for task-specific architecture engineering. The same architecture
achieves strong results on tasks spanning natural language and visual
understanding, multi-task and multi-modal reasoning, and StarCraft II. As
highlights, Perceiver IO outperforms a Transformer-based BERT baseline on the
GLUE language benchmark despite removing input tokenization and achieves
state-of-the-art performance on Sintel optical flow estimation with no explicit
mechanisms for multiscale correspondence.

本文提出 Perceiver IO 机器学习模型，它具有灵活的查询机制，能够处理来自任意领域的数据并对多种任务进行推理，其性能表现优于传统的 Transformer-based BERT 基准测试，并在 Sintel 光流估计方面取得了最先进的性能。