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Biological systems perceive the world by simultaneously processinghigh-dimensional inputs from modalities as diverse as vision, audition, touch,proprioception, etc. The perception models used in deep learning on the otherhand are designed for individual modalities, often relying on domain-specificassumptions such as the local grid structures exploited by virtually allexisting vision models. These priors introduce helpful inductive biases, butalso lock models to individual modalities. In this paper we introduce thePerceiver - a model that builds upon Transformers and hence makes fewarchitectural assumptions about the relationship between its inputs, but thatalso scales to hundreds of thousands of inputs, like ConvNets. The modelleverages an asymmetric attention mechanism to iteratively distill inputs intoa tight latent bottleneck, allowing it to scale to handle very large inputs. Weshow that this architecture is competitive with or outperforms strong,specialized models on classification tasks across various modalities: images,point clouds, audio, video, and video+audio. The Perceiver obtains performancecomparable to ResNet-50 and ViT on ImageNet without 2D convolutions by directlyattending to 50,000 pixels. It is also competitive in all modalities inAudioSet.

Perceiver: General Perception with Iterative Attention