Few-shot Instance Segmentation

Few-shot Instance Segmentation is a subtask in the field of computer vision that aims to achieve precise segmentation of specific instances with only a few annotated samples. The goal of this task is to quickly learn and recognize new object categories under limited data conditions, thereby enhancing the model's generalization and adaptability. Its application value lies in effectively reducing the cost of large-scale data annotation and accelerating the deployment of models in new scenarios, making it suitable for high-precision-demanding fields such as medical image analysis and autonomous driving.