# Meta-Transformer: A Unified Framework for Multimodal Learning

**Authors:** Yiyuan Zhang, Kaixiong Gong, Kaipeng Zhang, Hongsheng Li, Yu Qiao, Wanli Ouyang, Xiangyu Yue  
**arXiv:** [2307.10802](https://arxiv.org/abs/2307.10802)  
**Date:** 2023-07-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.10802v1/

## Key points

- The field of multimodal learning aims to process and relate information from multiple modalities.
- Designing a unified network for various modalities is challenging due to inherent gaps between them.
- The authors propose a framework called Meta-Transformer for multimodal learning.
- Meta-Transformer leverages a frozen encoder for multimodal perception without paired training data.
- The framework consists of a data tokenizer, modality-shared encoder, and task-specific heads.
- Meta-Transformer enables unified learning across 12 modalities with unpaired data.
- Experiments on various benchmarks demonstrate the effectiveness of Meta-Transformer across different tasks and modalities.
- This work represents an important step towards developing unified multimodal intelligence using transformers.
- The availability of code on GitHub allows further exploration and application of the framework.

## Summary

The field of multimodal learning aims to develop models that can effectively process and relate information from multiple modalities, such as natural language, images, point clouds, audio, video, time series, and tabular data. However, designing a unified network that can handle various modalities remains challenging due to the inherent gaps between them. In this work titled "Meta-Transformer: A Unified Framework for Multimodal Learning," authors Yiyuan Zhang, Kaixiong Gong, Kaipeng Zhang, Hongsheng Li, Yu Qiao, Wanli Ouyang and Xiangyu Yue propose a novel framework called Meta-Transformer. The key idea behind Meta-Transformer is to leverage a frozen encoder to perform multimodal perception without requiring paired multimodal training data. The framework consists of three main components: a unified data tokenizer; a modality-shared encoder with frozen parameters; and task-specific heads for downstream tasks. By mapping raw input data from different modalities into a shared token space and extracting high-level semantic features using the frozen encoder; Meta-Transformer enables unified learning across 12 modalities even with unpaired data. To evaluate the effectiveness of Meta-Transformer; the authors conduct experiments on various benchmarks covering fundamental perception tasks (text; image; point cloud audio); practical applications (X-Ray imaging infrared imaging hyperspectral imaging IMU); and data mining tasks (graph analysis tabular data analysis time-series analysis). The results demonstrate that Meta-Transformer is capable of handling a wide range of tasks across different modalities. This work represents an important step towards developing unified multimodal intelligence using transformers. The proposed Meta-Transformer framework addresses the challenges associated with processing multiple modalities by leveraging a frozen encoder and shared token space representation. The availability of code on GitHub provides researchers and practitioners with an opportunity to further explore and apply this framework in their own projects.