Overlapped 10 1

Overlapped 10-1 is a multi-task learning framework used in the field of computer vision to enhance the generalization ability and efficiency of models. This method achieves deeper feature learning and better performance by sharing underlying feature representations and simultaneously optimizing multiple related tasks. The goal of Overlapped 10-1 is to reduce the model's reliance on large-scale annotated data and improve resource utilization through the synergistic effects between tasks, thereby demonstrating higher accuracy and robustness in practical applications. Its application value lies in effectively addressing the few-shot learning problem, enhancing the model's adaptability and scalability, making it suitable for scenarios such as image classification and object detection.