Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

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 …

Instruction tuning large language models (LLMs) using machine-generated instruction-
following data has been shown to improve zero-shot capabilities on new tasks, but the idea …

Large multimodal models (LMM) have recently shown encouraging progress with visual
instruction tuning. In this paper we present the first systematic study to investigate the design …

In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models
(LVLMs) designed to perceive and understand both texts and images. Starting from the …

The recent success of text-to-image synthesis has taken the world by storm and captured the
general public's imagination. From a technical standpoint, it also marked a drastic change in …

The Large Vision-Language Model (LVLM) has enhanced the performance of various
downstream tasks in visual-language understanding. Most existing approaches encode …

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …

We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to
previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone …

We introduce the Yi model family, a series of language and multimodal models that
demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and …