Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

In this report, we introduce the Gemini 1.5 family of models, representing the next generation
of highly compute-efficient multimodal models capable of recalling and reasoning over fine …

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 …

We present VideoPoet, a language model capable of synthesizing high-quality video, with
matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder …

We present the training recipe and results of scaling up PaLI-X, a multilingual vision and
language model, both in terms of size of the components and the breadth of its training task …

Computer vision models have known performance disparities across attributes such as
gender and skin tone. This means during tasks such as classification and detection, model …

This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that
compares favorably to similar models that are 10x larger. As part of arriving at this strong …

In recent years, the rapid advancement of machine learning (ML) models, particularly
transformer-based pre-trained models, has revolutionized Natural Language Processing …

It is crucial that image datasets for computer vision are representative and contain accurate
demographic information to ensure their robustness and fairness, especially for smaller …