Perceiver Series by Google DeepMind
The Perceiver Series is a family of general-purpose, modality-agnostic transformer models developed by Google DeepMind. Designed to process diverse data types—including text, images, audio, video, and point clouds—these models use a latent attention mechanism to handle high-dimensional inputs efficiently. The architecture decouples input size from model depth, enabling scalable performance across various tasks without needing modality-specific components.
Key Models:
Perceiver (2021):
The original model designed to handle multiple input types through unified cross-attention and self-attention over latent variables. It demonstrated strong performance on vision and audio tasks.Perceiver IO (2021):
An extended version allowing flexible output generation, suitable for structured outputs like language, text-to-image understanding, and multimodal question answering.Perceiver AR (2022):
Tailored for autoregressive generation, enabling long-context text processing and sequence modeling. It adapts the Perceiver architecture for tasks such as language modeling and audio generation.
Key Features:
Modality-Agnostic: Processes text, images, audio, and more in a unified way.
Efficient Scaling: Uses latent space attention to manage large inputs with reduced computational cost.
Long-Context Handling: Effective for tasks requiring memory over extended sequences.
Flexible Output Structure: Suitable for both classification and generation tasks.
Example Use Cases:
Long-context language modeling
Multimodal reasoning (e.g., visual question answering)
Audio classification and generation
Text summarization and structured output generation