Unsupervised Semantic Segmentation

Unsupervised semantic segmentation is an important task in computer vision that aims to classify each pixel in an image through model learning without relying on annotated ground truth data. The goal of this task is to enable the model to autonomously recognize and distinguish different object categories in the image, thereby achieving a fine-grained understanding of the image content. Unsupervised semantic segmentation holds significant value in applications such as autonomous driving, medical image analysis, and scene understanding, as it can substantially reduce the cost and time associated with manual annotation.