Biological systems perceive 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 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 performance
comparable to ResNet-50 and ViT on ImageNet without 2D convolutions by directly
attending to 50,000 pixels. It is also competitive in all modalities in
AudioSet.

本文介绍了 Perceiver 模型，它利用了不对关系进行假设的 Transformer，同时还能在处理非常大的输入时进行规模扩展，通过迭代地将输入压缩到一个紧凑的潜在瓶颈中，并且在图像、点云、音频、视频和视频 + 音频等多个模态的分类任务中达到了与最佳模型相当的性能。