Vision Transformer

7 Posts

Illustration shows different self-attention mechanisms used by Transformer-based AI models.
Vision Transformer

Attention to Rows and Columns: Altering Transformers' Self-Attention Mechanism for Greater Efficiency

A new approach alters transformers' self-attention mechanism to balance computational efficiency with performance on vision tasks.
2 min read
Object-Detection Transformers Simplified: New Research Improves Object Detection With Vision Transformers
Vision Transformer

Object-Detection Transformers Simplified: New Research Improves Object Detection With Vision Transformers

ViTDet, a new system from Facebook, adds an object detector to a plain pretrained transformer.
2 min read
A series of graphs show the carbon emissions associated with training AI models.
Vision Transformer

Cutting the Carbon Cost of Training: A New Tool Helps AI Developers Lower Their Greenhouse Gas Emissions

You can reduce your model’s carbon emissions by being choosy about when and where you train it.
2 min read
Diagram
Vision Transformer

Who Was That Masked Input?

Researchers have shown that it’s possible to train a computer vision model effectively on around 66 percent of the pixels in each training image. New work used 25 percent, saving computation and boosting performance to boot.
2 min read
Upgrade for Vision Transformers
Vision Transformer

Upgrade for Vision Transformers

Vision Transformer and models like it use a lot of computation and memory when processing images. New work modifies these architectures to run more efficiently while adopting helpful properties from convolutions.
2 min read
Less Data for Vision Transformers
Vision Transformer

Less Data for Vision Transformers

Vision Transformer (ViT) outperformed convolutional neural networks in image classification, but it required more training data. New work enabled ViT and its variants to outperform other architectures with less training data.
2 min read
MobileNet
Vision Transformer

High Accuracy at Low Power

Equipment that relies on computer vision while unplugged — mobile phones, drones, satellites, autonomous cars — need power-efficient models. A new architecture set a record for accuracy per computation.
2 min read

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