Image to Text Generation

Image-to-text generation aims to automatically create descriptive text from images, a crucial task bridging computer vision and natural language processing. Current research focuses on improving the accuracy, fluency, and contextual understanding of generated captions, exploring various model architectures including transformer-based models, diffusion models, and retrieval-augmented approaches. Significant efforts are also dedicated to developing robust evaluation metrics and addressing challenges like bias, safety concerns (e.g., NSFW content), and the generation of hallucinated details. Advances in this field have broad implications for applications such as image search, accessibility tools for the visually impaired, and automated content creation.