Multimodal Text Generation

Multimodal text generation focuses on creating textual descriptions from various input modalities, such as images, audio, and video, aiming to improve the accuracy, fluency, and relevance of generated text. Current research emphasizes developing models that effectively integrate information across modalities, often employing large language models and transformer architectures, and exploring techniques like prompt engineering and diffusion models to enhance control and diversity in generation. This field is significant for advancing human-computer interaction, enabling applications like automated captioning, product description generation, and interactive robotic systems, and driving progress in understanding cross-modal relationships within artificial intelligence.