# Detailed Image Captioning and Hashtag Generation
## 2 Background
### Hashtag Generation  
Hashtag generation is an important task for social media content creation and organization. Unlike image captioning, which aims to produce natural language descriptions, hashtag generation focuses on producing concise, relevant tags to categorize and make content discoverable.  
Several approaches have been proposed for hashtag generation from visual and textual content. In [PERSON] and [PERSON] [21], a method for hashtag recommendation for multimodal microblog posts was developed. Their approach uses both visual features from images and textual features from post content to generate relevant hashtags. They employed a learning-to-rank framework to recommend hashtags based on their relevance scores. In [PERSON] and [PERSON] [22], a voting deep neural network combined with associative rules mining for image hashtag recommendations was proposed. Their method uses an ensemble of convolutional neural networks to extract visual features, which are then used to predict relevant hashtags through a voting mechanism. Association rule mining is applied to discover the relationships between hashtags. Some approaches leverage image captioning as an intermediate step. For example, the method proposed by [PERSON] et al. [23] first generates image captions using a CNN-RNN model, then applies natural language processing techniques to extract keywords from the captions and convert them into hashtags. This two-step approach allows leveraging advances in image captioning while tailoring the output for hashtag generation.  
Keyword extraction techniques can be adapted for hashtag generation from textual content like captions. As discussed by [PERSON] et al. [24], there are several approaches to keyword extraction. Statistical methods like TF-IDF, KPMiner, and YAKE use statistical features of the text to identify important keywords. These methods are computationally efficient but may miss semantic relationships. Graph-based methods such as TextRank and PositionRank construct graphs representing word relationships and use graph algorithms to extract key terms. These can capture some semantic information but may struggle with longer texts. More recent deep learning methods like KeyBERT [25] leverage pre-trained language models to extract keywords based on semantic similarity. These can potentially capture deeper semantic relationships but require more computational resources.  
There are several limitations to current hashtag generation approaches. Many methods struggle to incorporate broader contextual information beyond the immediate visual or textual content, which can lead to generic or irrelevant hashtags. Hashtag usage on social media evolves rapidly, and static models may fail to capture emerging trends or platform-specific hashtag cultures. User preferences for hashtag style and content vary widely, but most methods do not account for individual user preferences or posting history.  
The evaluation of hashtag generation models presents its own challenges. As noted by [PERSON] et al. [24], evaluating the quality of generated keywords (which can be applied to hashtags) is challenging as relevance can be subjective and context-dependent. Traditional metrics like precision, recall, and F1-score may not fully capture the quality of generated hashtags in a real-world social media context.  
Data quality and bias are also significant concerns. Training data for hashtag generation often comes from social media platforms, which can introduce biases and quality issues that affect model performance. Additionally, some of the more advanced deep learning approaches require significant computational resources, limiting their applicability in real-time or mobile scenarios [23]. Addressing these limitations presents opportunities for future research in hashtag generation, particularly in developing more context-aware, personalized, and computationally efficient approaches.