The online negative prompting strategy, while innovative, has several potential limitations. One significant limitation is its reliance on the synthesized low-resolution (LR) images as negative samples. If the synthesis process does not accurately represent the range of real-world degradations, the model may not learn effectively to distinguish between high-quality and low-quality images. This could lead to a lack of robustness when encountering unseen degradation types during inference.
Additionally, the fixed set of negative prompts used in the training process may not cover the full spectrum of possible image degradations. This limitation can hinder the model's ability to generalize across diverse scenarios, particularly in real-world applications where degradation can vary widely.
To improve the online negative prompting strategy, one approach could involve dynamically generating negative prompts based on the specific characteristics of the input images. By analyzing the degradation patterns in real-time, the model could adaptively select or generate negative prompts that are more representative of the current input's quality. This would enhance the model's ability to learn from a broader range of degradation types.
Moreover, incorporating a feedback mechanism that allows the model to learn from its mistakes during inference could further refine the negative prompting strategy. By analyzing the discrepancies between generated outputs and expected results, the model could adjust its understanding of what constitutes poor quality, leading to more effective training and improved performance across diverse degradation scenarios.