The current research in image captioning and image restoration is notably advancing through the integration of text-based models and diffusion techniques. A significant trend is the use of text as a robust auxiliary representation to enhance model generalization in real-world scenarios, addressing issues like 'generative capability deactivation' in out-of-distribution data. This approach leverages the richness and relevance of textual descriptions to guide image restoration processes, thereby improving the realism and accuracy of recovered images. Additionally, the field is witnessing innovations in data synthesis for scene text recognition, where diffusion models are being employed to generate high-quality, realistic text images, overcoming the limitations of traditional synthetic data. These advancements not only enhance the performance of models in controlled environments but also significantly improve their adaptability to diverse real-world conditions.

Noteworthy contributions include a target-aware prompting strategy in image captioning that mitigates overfitting by integrating object information, and a training-free framework for blind inverse problems that leverages text-to-image diffusion models, demonstrating broad applicability across various image restoration tasks.