Universal architectures like Mask2Former have redefined the way we approach image segmentation tasks. Traditionally, specialized architectures were used for specific tasks such as semantic, instance, and panoptic segmentation. Now, a single, unified architecture can outperform these task-specific models, offering benefits in performance, efficiency, and effort, while also reshaping the way we perceive these tasks. In this paper, experiments are conducted using the Mask2Former configuration for \textit{semantic segmentation}. However, similar to other universal models like DETR, these architectures, despite sharing the same underlying structure, \textit{are still trained separately for different tasks and datasets}. Recent works on the passage from the Universal Approximation Theorem to a Kolmogorov-Arnold theorem inspired the present work to delve in Kolmogorov Arnold Network on computer vision tasks.