The 4M framework, developed by the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and Apple, provides an efficient and scalable solution for the training of multi-modal vision basic models. The framework cleverly utilizes Transformer technology and processes different types of input data through modality-specific taggers, effectively overcoming many challenges of cross-modal training. Its innovation lies in the use of input and target masks for training, showing excellent performance in multiple visual tasks.
The 4M framework jointly launched by the Ecole Polytechnique Fédérale de Lausanne in Switzerland and Apple solves the challenge of training basic models of vision across multiple modalities. The framework uses Transformer technology to process multiple input modalities through modality-specific taggers, improving scalability and efficiency. Trained with input and target masks, 4M performs well on multiple vision tasks, showing great potential.
The emergence of the 4M framework marks significant progress in multi-modal vision basic model training technology and provides a solid foundation for the expansion of future artificial intelligence applications. Its efficiency and scalability will promote the emergence of more innovative applications and deserve continued attention and in-depth research.