Generative Multimodal Models are In-Context Learners
Quan Sun Yufeng Cui Xiaosong Zhang Fan Zhang Qiying Yu Zhengxiong Luo Yueze Wang Yongming Rao Jingjing Liu Tiejun Huang Xinlong Wang
The human ability to easily solve multimodal tasks in context (i.e., with
only a few demonstrations or simple instructions), is what current multimodal
systems have largely struggled to imitate. In this work, we demonstrate that
the task-agnostic in-context learning capabilities of large multimodal models
can be significantly enhanced by effective scaling-up. We introduce Emu2, a
generative multimodal model with 37 billion parameters, trained on large-scale
multimodal sequences with a unified autoregressive objective. Emu2 exhibits
strong multimodal in-context learning abilities, even emerging to solve tasks
that require on-the-fly reasoning, such as visual prompting and object-grounded
generation. The model sets a new record on multiple multimodal understanding
tasks in few-shot settings. When instruction-tuned to follow specific
instructions, Emu2 further achieves new state-of-the-art on challenging tasks
such as question answering benchmarks for large multimodal models and
open-ended subject-driven generation. These achievements demonstrate that Emu2
can serve as a base model and general-purpose interface for a wide range of
multimodal tasks. Code and models are publicly available to facilitate future
research.