arxiv preprint – Meta-Transformer: A Unified Framework for Multimodal Learning

In this episode we discuss Meta-Transformer: A Unified Framework for Multimodal Learning
by Yiyuan Zhang, Kaixiong Gong, Kaipeng Zhang, Hongsheng Li, Yu Qiao, Wanli Ouyang, Xiangyu Yue. The paper presents a framework called Meta-Transformer for processing multiple modalities in multimodal learning. It uses a frozen encoder for feature extraction across different modalities, including natural language, images, audio, and more. The Meta-Transformer framework demonstrates the potential of transformer architectures in achieving unified multimodal intelligence.