Integrating Semi-Supervised and Active Learning for Semantic Segmentation

arXiv:2501.19227v2 Announce Type: replace-cross Abstract: In this paper, we propose a novel active learning approach integrated with an improved semi-supervised learning framework to reduce the cost of manual annotation and enhance model performance. Our proposed approach effectively leverages both the labelled data selected through active learning and the unlabelled data excluded from the selection process. The proposed active learning approach pinpoints areas where the pseudo-labels are likely