Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting
unseen classes with only a handful of annotations. Previous methods limited to the semantic …

The booming of 3D recognition in the 2020s began with the introduction of point cloud
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …

Semantic segmentation is still a challenging task for parsing diverse contexts in different
scenes, thus the fixed classifier might not be able to well address varying feature …

In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or
both. However, navigating the multitude of available architectures for automatic …

Image manipulation localization (IML), which seeks to accurately segment tampered regions
that are artfully fastened into a normal image, is a fundamental yet challenging computer …

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal
abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal …

Thanks to the advantages of the friendly annotations and the satisfactory performance,
weakly-supervised semantic segmentation (WSSS) approaches have been extensively …

Abstract This paper introduces Unified Language-driven Zero-shot Domain Adaptation
(ULDA) a novel task setting that enables a single model to adapt to diverse target domains …

Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The
development of automated methods for nuclei segmentation enables quantitative analysis of …

In this paper, we propose a novel image forgery detection paradigm for boosting the model
learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared …