Generative Multimodal Models are In-Context Learners
This addresses the challenge of enabling AI systems to perform multimodal tasks with minimal demonstrations, which is incremental as it builds on scaling existing approaches.
The paper tackles the problem of enhancing multimodal in-context learning by scaling up models, introducing Emu2, a 37-billion parameter generative multimodal model that sets new records on multiple few-shot multimodal understanding tasks and achieves state-of-the-art on challenging benchmarks like question answering and open-ended generation.
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.