  Image captioning bridges the gap between vision and language by automatically
generating natural language descriptions for images. Traditional image
captioning methods often overlook the preferences and characteristics of users.
Personalized image captioning solves this problem by incorporating user prior
knowledge into the model, such as writing styles and preferred vocabularies.
Most existing methods emphasize the user context fusion process by memory
networks or transformers. However, these methods ignore the distinct domains of
each dataset. Therefore, they need to update the entire caption model
parameters when meeting new samples, which is time-consuming and
calculation-intensive. To address this challenge, we propose a novel
personalized image captioning framework that leverages user context to consider
personality factors. Additionally, our framework utilizes the prefix-tuning
paradigm to extract knowledge from a frozen large language model, reducing the
gap between different language domains. Specifically, we employ CLIP to extract
the visual features of an image and align the semantic space using a
query-guided mapping network. By incorporating the transformer layer, we merge
the visual features with the user's contextual prior knowledge to generate
informative prefixes. Moreover, we employ GPT-2 as the frozen large language
model. With a small number of parameters to be trained, our model performs
efficiently and effectively. Our model outperforms existing baseline models on
Instagram and YFCC100M datasets across five evaluation metrics, demonstrating
its superiority, including twofold improvements in metrics such as BLEU-4 and
CIDEr.
