Multimodal Pre-training is a method of data pre-processing that uses multiple forms of data to extract meaningful information. This can be applied to natural language processing (NLP) tasks as well as computer vision tasks. It helps to train deep learning models from a limited training dataset by leveraging unlabeled data from different sources, such as images and text. In these cases, the models can learn from these different types of data, potentially reducing the amount of training data needed and improving results.
In NLP, multimodal pre-training can be used to create general language-understanding models such as BERT and GPT-2. These models can learn the language structure of different sentences and contexts, as well as the relationships between words, by combining representations from text, images, and more. This helps the model to understand the meaning of words and how they are used in different contexts in order to provide a better prediction or generate more meaningful natural language.
In computer vision, multimodal pre-training can be used to help models learn common visual cues, such as shapes, textures, and colors. This helps the model to recognize objects in a scene and classify them as part of a larger context. By combining different forms of data, the model can gain an understanding of how different elements in the scene are connected to each other to ensure an accurate prediction.
By using multimodal pre-training, deep learning models can be trained more effectively from limited training data. This in turn can reduce training time and improve overall model performance.
