Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …

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

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

We introduce LlamaGen, a new family of image generation models that apply original``next-
token prediction''paradigm of large language models to visual generation domain. It is an …

In this work we address various segmentation tasks each traditionally tackled by distinct or
partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently …

Scaling up the size of vision models has been the de facto standard to obtain more powerful
visual representations. In this work, we discuss the point beyond which larger vision models …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

Vision-language models (VLMs) are typically composed of a vision encoder, eg CLIP, and a
language model (LM) that interprets the encoded features to solve downstream tasks …

This paper introduces AIM, a collection of vision models pre-trained with an autoregressive
objective. These models are inspired by their textual counterparts, ie, Large Language …