One potential limitation of using synthetic captions is the risk of introducing noise and inaccuracies into the training data, which can negatively impact the model's performance. To improve the quality and reliability of the generated captions, the authors can consider the following strategies:
Data Augmentation Techniques: Implement data augmentation techniques to generate diverse and realistic synthetic captions, reducing the risk of hallucination or irrelevant information.
Adversarial Training: Incorporate adversarial training to identify and filter out noisy or misleading synthetic captions during the training process.
Human-in-the-Loop Validation: Introduce a human validation step to verify the quality of the synthetic captions generated by the model, ensuring that they are accurate and relevant to the images.
Fine-Tuning with Real Data: Fine-tune the model with a small amount of real caption data to help the model distinguish between synthetic and real captions, improving the overall captioning performance.
By implementing these strategies, the authors can enhance the quality and reliability of the synthetic captions, leading to more effective training and improved model performance.