In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …

As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

We study the capabilities of speech processing systems trained simply to predict large
amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

We launch EVA, a vision-centric foundation model to explore the limits of visual
representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …

Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …

The recent success of text-to-image synthesis has taken the world by storm and captured the
general public's imagination. From a technical standpoint, it also marked a drastic change in …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

As the core building block of vision transformers, attention is a powerful tool to capture long-
range dependency. However, such power comes at a cost: it incurs a huge computation …

Scaling up neural networks has led to remarkable performance across a wide range of
tasks. Moreover, performance often follows reliable scaling laws as a function of training set …