# Perceiver

> transformer for non-textual data

**Wikidata**: [Q108281205](https://www.wikidata.org/wiki/Q108281205)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Perceiver)  
**Source**: https://4ort.xyz/entity/perceiver

Here’s the structured knowledge entry for **Perceiver**:

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## Summary  
The Perceiver is a transformer-based machine-learning model designed to handle non-textual data, such as images, audio, and video. Developed by Google DeepMind, it extends the capabilities of traditional transformers by using iterative attention mechanisms to process diverse input formats efficiently.

## Key Facts  
- **Subclass of**: Transformer (machine-learning model architecture)  
- **Uses**: Attention mechanisms for processing data  
- **Developed by**: Google DeepMind  
- **Introduced in**: Described in the paper *Perceiver: General Perception with Iterative Attention*  
- **Primary application**: General perception tasks involving non-textual data (e.g., images, audio)  
- **Wikipedia availability**: Articles in English, Catalan, and Ukrainian  
- **Wikidata description**: "Transformer for non-textual data"  
- **Sitelink count**: 3  

## FAQs  
### Q: What makes Perceiver different from traditional transformers?  
A: Perceiver is optimized for non-textual data like images and audio, using iterative attention to handle diverse inputs, whereas traditional transformers are primarily designed for text.  

### Q: Who developed Perceiver?  
A: Perceiver was introduced by Google DeepMind, as detailed in their research paper *Perceiver: General Perception with Iterative Attention*.  

### Q: What types of data can Perceiver process?  
A: Perceiver is designed for general perception tasks, including images, audio, and video, leveraging its attention-based architecture.  

## Why It Matters  
The Perceiver represents a significant advancement in machine learning by adapting transformer architectures—originally developed for text—to non-textual data. This innovation broadens the applicability of transformers to domains like computer vision and audio processing, where traditional models faced limitations. By using iterative attention, Perceiver efficiently handles high-dimensional inputs, enabling more flexible and scalable perception systems. Its development underscores the growing trend of unifying disparate AI tasks under a single architectural framework.  

## Notable For  
- **Generalization**: Adapts transformers to non-textual data, unlike most transformer models focused on NLP.  
- **Iterative attention**: Uses repeated attention mechanisms to process diverse inputs efficiently.  
- **Google DeepMind**: Developed by a leading AI research lab, ensuring robust theoretical and practical foundations.  

## Body  
### Architecture  
- Based on the transformer model, originally developed by Google Brain.  
- Incorporates attention mechanisms to process high-dimensional non-textual data.  

### Development  
- Introduced by Google DeepMind.  
- Described in the paper *Perceiver: General Perception with Iterative Attention*.  

### Applications  
- Designed for general perception tasks, including images, audio, and video.  
- Addresses limitations of traditional transformers in handling non-textual inputs.  

### Availability  
- Wikipedia articles exist in English, Catalan, and Ukrainian.  
- Wikidata entry describes it as a "transformer for non-textual data."  

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