Vision Perceiver Learned
| Property | Value |
|---|---|
| Developer | DeepMind |
| Training Data | ImageNet (14M images, 1K classes) |
| Resolution | 224x224 |
| Performance | 72.7% Top-1 Accuracy |
| Paper | Perceiver IO Paper |
What is vision-perceiver-learned?
Vision Perceiver Learned is a transformer-based model that revolutionizes image processing by applying self-attention on a fixed set of latent vectors rather than directly on input pixels. This innovative approach allows the model to process images efficiently without the computational overhead typically associated with attention mechanisms scaling with input size.
Implementation Details
The model employs a unique architecture where it processes raw pixel values using learned 1D position embeddings, avoiding the need for image patching as seen in ViT models. It uses cross-attention between latent vectors and inputs, followed by self-attention among latents, making computational requirements independent of input size.
- Processes raw pixel values directly
- Uses learned 1D position embeddings
- Employs cross-attention and self-attention mechanisms
- Features flexible decoder queries for output generation
Core Capabilities
- Image classification across 1000 classes
- Feature extraction for downstream tasks
- Efficient processing of high-resolution images
- Flexible output generation through decoder queries
Frequently Asked Questions
Q: What makes this model unique?
The model's key innovation lies in its ability to process images without depending on the input size for computational complexity, achieved through its latent vector approach and learned position embeddings. It can handle raw pixel values directly, unlike models that require image patching.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks and feature extraction. It's particularly useful when you need to process high-resolution images efficiently or when you want to extract features for downstream computer vision tasks.
