The EATFormer architecture, with its innovative integration of Evolutionary Algorithms (EAs) and Transformer blocks, can be extended to various other computer vision tasks beyond traffic sign recognition. One potential application could be in object detection and localization tasks, where the model's ability to capture multi-scale, interactive, and individual information through its components can enhance the accuracy and efficiency of detecting objects in complex scenes. Additionally, the Modulated Deformable MSA module introduced in the EATFormer architecture can be leveraged for tasks requiring precise modeling of irregular locations, such as semantic segmentation or instance segmentation. By adapting the EATFormer backbone to different datasets and tasks, researchers can explore its effectiveness in tasks like image classification, object tracking, and scene understanding.