perceiver

Perceiver is a general‑purpose neural network architecture designed to process inputs from arbitrary modalities using a single model. It was introduced by researchers at DeepMind as a modality-agnostic approach to perception, aiming to unify how different data types are handled by a single framework.

The core idea of Perceiver is to replace conventional modality-specific encoders with a fixed-size latent array that interacts with the input through cross-attention. The input queries the latent array, producing a compact latent representation that is then refined through multiple layers of self-attention and feed-forward transformations. By operating in this latent space, the architecture decouples input size from compute, enabling the model to handle images, audio, point clouds, video frames, and other data without bespoke encoders for each modality.

Perceiver IO is an extension of the original design that adds learnable input and output adapters to map data to and from the latent space. This enables the model to perform a wider range of tasks with varied input and output formats, further broadening its applicability across multimodal tasks and supervised learning scenarios.

Impact and use of Perceiver include its demonstration of competitive performance on multimodal benchmarks and its status as a scalable, modality-agnostic alternative to traditional, modality-specific architectures. The approach has influenced follow-up work in scalable perceptual models and multimodal machine learning, highlighting the possibility of unifying perception under a single architectural framework.

Limitations and considerations include potential trade-offs relative to highly specialized models in certain domains, as well as the training complexity and dependence on data quality and scale. As with many machine learning architectures, practical performance depends on task settings and optimization choices.