Pseudo Mask

Pseudo masks are synthetically generated segmentation masks used to train or improve image segmentation models, particularly in weakly supervised or unsupervised settings where obtaining fully annotated data is expensive or impossible. Current research focuses on generating high-quality pseudo masks using various techniques, including clustering algorithms, generative models (like Stable Diffusion), and attention mechanisms within CNNs and Vision Transformers, often incorporating strategies to mitigate inherent noise and uncertainty. This work is significant because it enables the development of accurate segmentation models with minimal human annotation, impacting fields like medical image analysis and autonomous driving where large, fully labeled datasets are often unavailable.