High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

In this work, we for the first time present a method for detecting labeling errors in image
datasets with semantic segmentation, ie, pixel-wise class labels. Annotation acquisition for …

Segment anything model (SAM) has emerged as the leading approach for zero-shot
learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It …

When the traditional semantic segmentation model is adopted, the different feature
importance of feature maps is ignored in the feature extraction stage, which results in the …

Relying on large-scale training data with pixel-level labels, previous edge detection
methods have achieved high performance. However, it is hard to manually label edges …

Deep neural networks typically require accurate and a large number of annotations to
achieve outstanding performance in medical image segmentation. One-shot and weakly …

Deep-learning-based automatic defect segmentation is one of the hot research areas in
computer vision application for the task of intelligent industrial inspection. Recently, several …

Semi-supervised semantic segmentation methods use a small amount of clean pixel-level
annotations to guide the interpretation of a larger quantity of unlabelled image data. The …

This paper studies a new yet practical setting of semi-supervised semantic segmentation, ie,
hybrid-supervised semantic segmentation, where a small number of pixel-level (strong) …

Semantic segmentation is an important and popular research area in computer vision that
focuses on classifying pixels in an image based on their semantics. However, supervised …