Image-text Classification

Image-text Classification is a multimodal learning task aimed at combining image and text information to perform comprehensive classification. The goal of this task is to train models to identify and understand the correlations between images and texts, thereby enhancing the accuracy and robustness of classification. In practical applications, Image-text Classification can be widely used in content moderation, sentiment analysis, product recommendations, and other fields, improving the level of intelligent decision-making.