3 accepted papers
Weakly-supervised semantic segmentation, which achieves pixel-wise segmentation using image-level labels, has emerged as an alternative to fully supervised methods by reducing the need for detailed annotations. Inspired by the recent success of the teacher-student strategy in various vision tasks, w…
Instance segmentation requires labor-intensive manual labeling of the contours of complex objects in images for training. The labels can also be provided incrementally in practice to balance the human labor in different time steps. However, research on incremental learning for instance segmentation…
A continual cell instance segmenter aims to continually learn to segment new objects while preserving the ability to localize and distinguish old objects without access to previous data. Besides catastrophic forgetting, background shift, where the background class could contain objects in the old an…