Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Several image processing tasks, such as image classification and object detection, have
been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and …

The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Building models that can be rapidly adapted to novel tasks using only a handful of annotated
examples is an open challenge for multimodal machine learning research. We introduce …

Visually-situated language is ubiquitous—sources range from textbooks with diagrams to
web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to …

Abstract A Vision Transformer (ViT) is a simple neural architecture amenable to serve
several computer vision tasks. It has limited built-in architectural priors, in contrast to more …

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners
for computer vision. Our MAE approach is simple: we mask random patches of the input …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

The influential Residual Networks designed by He et al. remain the gold-standard
architecture in numerous scientific publications. They typically serve as the default …