One potential drawback of SLiMe's approach compared to traditional supervised methods is its performance on extremely limited samples. While SLiMe excels at one-shot segmentation tasks with just a single annotated sample, it may struggle when faced with highly complex images that require more nuanced segmentation details. Additionally, the reliance on attention maps extracted from Stable Diffusion models may introduce noise or inaccuracies in the segmentation results if not carefully optimized. Moreover, the need for additional regularization terms and careful tuning of hyperparameters adds complexity to the optimization process.