Unsupervised Semantic Segmentation

Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions without relying on labeled data, addressing the significant cost and effort of manual annotation. Current research focuses on leveraging pre-trained models, particularly vision transformers, and employing techniques like contrastive learning, clustering algorithms (e.g., k-means, spectral clustering), and iterative refinement to generate pseudo-labels and improve segmentation accuracy. This field is crucial for expanding the applicability of semantic segmentation to diverse domains with limited labeled data, impacting applications ranging from medical image analysis to autonomous driving.