Vision-language models (VLMs) such as CLIP have shown promising performance on a
variety of recognition tasks using the standard zero-shot classification procedure--computing …

We have now entered the era of trillion parameter machine learning models trained on
billion-sized datasets scraped from the internet. The rise of these gargantuan datasets has …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Object detection is a crucial task in computer vision systems with a wide range of
applications in autonomous driving, medical imaging, retail, security, face recognition …

As machine learning (ML) increasingly affects people and society, awareness of its potential
unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable …

The Open Images Dataset contains approximately 9 million images and is a widely accepted
dataset for computer vision research. As is common practice for large datasets, the …

There has been a surge of recent interest in sociocultural diversity in machine learning
research. Currently, however, there is a gap between discussions of measures and benefits …

Echoing the evolving interest and impact of artificial intelligence on society, governments are
increasingly looking for ways to strategically position themselves as both innovators and …

Machine learning (ML) and artificial intelligence (AI) tools increasingly permeate every
possible social, political, and economic sphere; sorting, taxonomizing and predicting …

Computer vision often treats perception as objective, and this assumption gets reflected in
the way that datasets are collected and models are trained. For instance, image descriptions …