Image to Text Mapping

Image-to-text mapping focuses on automatically generating textual descriptions from images or, conversely, creating images from text descriptions, aiming to bridge the semantic gap between visual and linguistic modalities. Current research emphasizes improving the accuracy and efficiency of this mapping using various techniques, including transformer-based models, contrastive learning, and retrieval-augmented methods that incorporate object-level details. These advancements are crucial for applications ranging from large-scale image indexing and retrieval to more sophisticated tasks like multimodal creative content generation and open-vocabulary object detection, ultimately enhancing human-computer interaction and artificial intelligence capabilities.