Image Text Multimodal

Image-text multimodal research focuses on developing models that effectively understand and generate content combining images and text. Current efforts concentrate on creating larger, higher-quality datasets for training, employing deep neural network architectures like transformers and convolutional neural networks to integrate visual and textual information, and refining evaluation metrics to assess model performance across diverse tasks. This field is significant because it advances artificial intelligence's ability to interpret and create rich multimodal content, with applications ranging from content generation and analysis to improved search and information retrieval.