X Former

X-Formers represent a burgeoning area of research focusing on adapting the transformer architecture for diverse applications beyond natural language processing. Current work centers on improving visual understanding in multimodal models by combining contrastive and reconstruction learning techniques, developing specialized transformers for tasks like object tracking, change detection, and medical image segmentation, and addressing computational challenges through efficient designs and hardware acceleration. This research significantly impacts various fields, enabling advancements in areas such as computer vision, medical imaging, and cybersecurity through improved accuracy, efficiency, and explainability of AI models.