Weakly Supervised Referring Expression Segmentation

Weakly Supervised Referring Expression Segmentation is a sub-task in the field of computer vision that aims to achieve pixel-level segmentation of objects referred to by natural language expressions using limited or incomplete annotated data. The goal of this task is to leverage weakly supervised learning methods to improve the model's generalization ability and segmentation accuracy under conditions of limited annotations, thereby reducing the cost and time associated with large-scale data annotation. Its application value lies in effectively enhancing the performance of image understanding and human-computer interaction systems, especially in scenarios such as medical image analysis, autonomous driving, and robotic vision.