Visual-Textual Fusion

Visual-textual fusion is a method that combines information from images and written text to understand and analyze data better. By integrating visual cues with descriptive language, systems can interpret context more accurately—such as recognizing objects in a photo along with their descriptions. This approach improves tasks like image captioning, visual question answering, and multimedia retrieval, providing a richer and more comprehensive understanding similar to how humans interpret visual and textual information together. It enables machines to process and reason about visual and textual data simultaneously for more intelligent insights.