  In-context learning (ICL) involves reasoning from given contextual examples.
As more modalities comes, this procedure is becoming more challenging as the
interleaved input modalities convolutes the understanding process. This is
exemplified by the observation that multimodal models often struggle to
effectively extrapolate from contextual examples to perform ICL. To address
these challenges, we introduce MultiModal In-conteXt Tuning (M$^2$IXT), a
lightweight module to enhance the ICL capabilities of multimodal unified
models. The proposed M$^2$IXT module perceives an expandable context window to
incorporate various labeled examples of multiple modalities (e.g., text, image,
and coordinates). It can be prepended to various multimodal unified models
(e.g., OFA, Unival, LLaVA) of different architectures and trained via a
mixed-tasks strategy to enable rapid few-shot adaption on multiple tasks and
datasets. When tuned on as little as 50K multimodal data, M$^2$IXT can boost
the few-shot ICL performance significantly (e.g., 18\% relative increase for
OFA), and obtained state-of-the-art results across an array of tasks including
visual question answering, image captioning, visual grounding, and visual
entailment, while being considerably small in terms of model parameters (e.g.,
$\sim$$20\times$ smaller than Flamingo or MMICL), highlighting the flexibility
and effectiveness of M$^2$IXT as a multimodal in-context learner.
