The fusion strategy of multimodal learning in image and text recognition

Multimodal data such as text and image play an important role in various fields. Traditional machine learning methods often only deal with the data of a single modality, while ignoring the relevance between different modalities, which limits the ability to fully understand and analyze the data. Therefore, multimodal learning emerges as the times require, which aims to improve the effectiveness of data analysis and decision-making by fusing the information of different modal data. In this paper, the basic idea, fusion strategy and specific steps of multimodal learning are described in detail, including data fusion, feature extraction, model training and evaluation. Through a case study, it shows how to combine natural language processing and computer vision technology to solve the joint recognition task of image and text. The importance of selecting a suitable data fusion method is emphasized, and a method for evaluating the effect of multi-modal data processing is proposed.