Unsupervised Semantic Segmentation With

Unsupervised Semantic Segmentation with Language-Image Pre-training is a computer vision task aimed at achieving pixel-level semantic segmentation through features pre-trained on image-text pairs, without the use of human-level semantic supervision. The goal of this task is to automatically learn the boundaries and categories of different objects in images, thereby enhancing the model's generalization capability on unlabeled data. Its application value lies in significantly reducing annotation costs and improving model performance on large datasets, making it suitable for scenarios such as autonomous driving and medical image analysis.