In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Abstract We present Visual AutoRegressive modeling (VAR), a new generation paradigm
that redefines the autoregressive learning on images as coarse-to-fine" next-scale …

Humans can easily solve multimodal tasks in context with only a few demonstrations or
simple instructions which current multimodal systems largely struggle to imitate. In this work …

We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of
understanding spatial referring of any shape or granularity within an image and accurately …

We present Unified-IO 2 a multimodal and multi-skill unified model capable of following
novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can …

We present Emu, a Transformer-based multimodal foundation model, which can seamlessly
generate images and texts in multimodal context. This omnivore model can take in any …

Recent advancements in generative models have highlighted the crucial role of image
tokenization in the efficient synthesis of high-resolution images. Tokenization, which …

This paper presents DreamLLM, a learning framework that first achieves versatile
Multimodal Large Language Models (MLLMs) empowered with frequently overlooked …