Pixel Level Pseudo Label

Pixel-level pseudo-labeling is a crucial technique in semi-supervised and weakly-supervised image segmentation and object detection, aiming to leverage unlabeled data by assigning pixel-level class labels predicted by a model. Current research focuses on improving the reliability of these pseudo-labels, often employing techniques like confidence thresholding, attention mechanisms (especially within Vision Transformers), and multi-clue consistency learning to address issues like noisy boundaries and inconsistent predictions across different object scales and orientations. These advancements significantly reduce the reliance on expensive pixel-level annotations, impacting various applications, including medical image analysis and remote sensing, by enabling the training of accurate models with limited labeled data.