This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Large-scale pre-training and instruction tuning have been successful at creating general-
purpose language models with broad competence. However, building general-purpose …

Abstract We present the Pathways [1] Autoregressive Text-to-Image (Parti) model, which
generates high-fidelity photorealistic images and supports content-rich synthesis involving …

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 …

Large language models (LLMs) have notably accelerated progress towards artificial general
intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing …

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 …

Multimodal Large Language Model (MLLM) recently has been a new rising research
hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform …

Abstract Vision-Language Pre-training (VLP) has advanced the performance for many vision-
language tasks. However, most existing pre-trained models only excel in either …

In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify
vision-language tasks such as image/video captioning and question answering. While …