Vision Transformer

Generated images can be more effective than real ones in training a vision model to classify images. Yonglong Tian, Lijie Fan, and colleagues at Google and MIT introduced StableRep, a self-supervised method that trains vision transformers on images generated by...

Vision transformers have bested convolutional neural networks (CNNs) in a number of key vision tasks. Have CNNs hit their limit? New research suggests otherwise.

Vision transformers typically process images in patches of fixed size. Smaller patches yield higher accuracy but require more computation. A new training method lets AI engineers adjust the tradeoff.

Work on vision transformers exploded in 2022. Researchers published well over 17,000 ViT papers during the year. A major theme: combining self-attention and convolution.

Vision Transformers (ViTs) are overtaking convolutional neural networks (CNN) in many vision tasks, but procedures for training them are still tailored for CNNs. New research investigated how various training ingredients affect ViT performance.

A new approach alters transformers' self-attention mechanism to balance computational efficiency with performance on vision tasks.

ViTDet, a new system from Facebook, adds an object detector to a plain pretrained transformer.

You can reduce your model’s carbon emissions by being choosy about when and where you train it.

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.

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.

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.

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.