Multi Modal Pre Training

Multi-modal pre-training aims to leverage the power of large language models by extending them to incorporate various data modalities, such as images, videos, and 3D point clouds, for improved representation learning. Current research focuses on developing efficient architectures, like mixture-of-experts models and retrieval-augmented methods, to handle the complexity of aligning heterogeneous data and improve pre-training efficiency. This approach is significantly impacting diverse fields, enhancing performance in tasks ranging from medical image analysis and automatic speech recognition to 3D object understanding and embodied AI, by enabling more robust and data-efficient models. The resulting models demonstrate improved performance on downstream tasks compared to unimodal approaches.