  Biological systems understand the world by simultaneously processing
high-dimensional inputs from modalities as diverse as vision, audition, touch,
proprioception, etc. The perception models used in deep learning on the other
hand are designed for individual modalities, often relying on domain-specific
assumptions such as the local grid structures exploited by virtually all
existing vision models. These priors introduce helpful inductive biases, but
also lock models to individual modalities. In this paper we introduce the
Perceiver - a model that builds upon Transformers and hence makes few
architectural assumptions about the relationship between its inputs, but that
also scales to hundreds of thousands of inputs, like ConvNets. The model
leverages an asymmetric attention mechanism to iteratively distill inputs into
a tight latent bottleneck, allowing it to scale to handle very large inputs. We
show that this architecture performs competitively or beyond strong,
specialized models on classification tasks across various modalities: images,
point clouds, audio, video and video+audio. The Perceiver obtains performance
comparable to ResNet-50 on ImageNet without convolutions and by directly
attending to 50,000 pixels. It also surpasses state-of-the-art results for all
modalities in AudioSet.
