While models trained to mimic human saliency offer significant benefits in terms of scalability and efficiency, there are potential limitations and drawbacks to consider. One limitation is the inherent bias or limitations in the training data used to train these models. If the training data does not adequately represent the diversity of human saliency patterns, the generated saliency maps may not capture the full range of relevant features. Additionally, models trained to mimic human saliency may not possess the same level of nuanced understanding or contextual awareness as human annotators. Human annotators can provide subjective insights and domain expertise that may be challenging to replicate in an automated model. Moreover, the interpretability and explainability of saliency maps generated by these models may be limited compared to human annotations. Therefore, while models trained to mimic human saliency offer scalability advantages, they should be used judiciously in conjunction with human-sourced saliency to ensure comprehensive and accurate training.