Meta-Transformer is a unified multimodal learning framework that uses a single frozen transformer encoder to handle diverse data modalities. The approach maps different modality inputs into a shared token space and processes them through a standard transformer backbone without any modality-specific modifications. It was published at ICCV 2023 and has received significant citations, demonstrating broad research community interest in unified multimodal architectures.