# Detailed Image Captioning and Hashtag Generation
## 5 Conclusions  
In this paper, we have presented CapFlow, an integrated approach to detailed image captioning and hashtag generation for automated content analysis and management. The key contributions of this work can be summarized as follows.  
First, we presented an efficient detailed image captioning model. By fine tuning the Florence-2 model using Low-Rank Adaptation (LoRA) with carefully selected datasets, particularly DOCCI, state-of-the-art performance in generating detailed image captions was achieved. Model selection was achieved through comprehensive evaluation of numerous state-of-the-art image captioning models. The selected model outperformed larger, more complex models in the CAPTURE metric (which is specifically designed to evaluate detailed captions), while maintaining computational efficiency suitable for real-time applications.  
Second, we developed an effective hashtag generation method. The use of KeyBERT for hashtag generation from detailed captions demonstrated superior performance compared to alternative methods. Despite the inherent challenges in hashtag generation tasks, the proposed approach achieves a balance between relevance and diversity in generated hashtags, outperforming other tested methods in precision, recall, and F1-score.  
Lastly, we demonstrated the implementation of the CapFlow into a Chrome extension by integrating detailed image captioning and hashtag generation into a unified, browser -based system. This application demonstrates the potential for enhancing content creation workflows and improving web accessibility without requiring powerful hardware.  
The experimental results highlight several important findings. The DOCCI-adapted Florence-2 model achieved a CAPTURE score of 0.5757, surpassing both the base model and larger, more complex vision-language models (VLMs). This performance was achieved with an average processing time of only 0.688 s, demonstrating a remarkable balance between accuracy and efficiency. The KeyBERT-based hashtag generation method achieved a precision of 0.6132 and an F1-score of 0.5932, outperforming other tested methods. While these scores might seem modest in traditional text classification contexts, they represent strong performance in the more subjective and open-ended task of hashtag generation.  
These contributions have several important implications for the field. The ability to generate detailed image captions quickly and accurately can significantly improve web accessibility for visually impaired users, providing richer context for screen readers and other assistive technologies. The integrated system can streamline content creation workflows for marketers, social media managers, and content creators, automating the process of generating detailed image descriptions and relevant hashtags. The efficiency of the proposed models demonstrates the potential for deploying advanced AI capabilities in resource-constrained environments, such as web browsers or mobile devices, making these technologies more widely accessible.  
Conceptualization, N.S. and Y.L.; Methodology, N.S.; Software, N.S.; Validation, N.S.; Formal analysis, N.S.; Investigation, N.S. and Y.L.; Data curation, N.S.; Writing--original draft, N.S.; Writing--review & editing, N.S. and Y.L.; Visualization, N.S.; Supervision, Y.L.; Project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.