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Hierarchical dense correlation distillation for few-shot segmentation
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 …
unseen classes with only a handful of annotations. Previous methods limited to the semantic …
Oa-cnns: Omni-adaptive sparse cnns for 3d semantic segmentation
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 …
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …
Unified language-driven zero-shot domain adaptation
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 …
(ULDA) a novel task setting that enables a single model to adapt to diverse target domains …
Transformer-auxiliary neural networks for image manipulation localization by operator inductions
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 …
that are artfully fastened into a normal image, is a fundamental yet challenging computer …
Learning context-aware classifier for semantic segmentation
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 …
scenes, thus the fixed classifier might not be able to well address varying feature …
Few shot medical image segmentation with cross attention transformer
Medical image segmentation has made significant progress in recent years. Deep learning-
based methods are recognized as data-hungry techniques, requiring large amounts of data …
based methods are recognized as data-hungry techniques, requiring large amounts of data …
BoNuS: boundary mining for nuclei segmentation with partial point labels
Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The
development of automated methods for nuclei segmentation enables quantitative analysis of …
development of automated methods for nuclei segmentation enables quantitative analysis of …
An improved object detection algorithm based on the Hessian matrix and conformable derivative
In this paper, a newfangled technique for edge detection that combines the Khalil
conformable derivative and the Hessian matrix is developed and experimentally validated …
conformable derivative and the Hessian matrix is developed and experimentally validated …
Anatomically guided cross-domain repair and screening for ultrasound Fetal biometry
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 …
abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal …
Extracting photovoltaic panels from heterogeneous remote sensing images with spatial and spectral differences
The accurate extraction of the installation area of the photovoltaic power station is an
important basis for the management of the photovoltaic power generation system. Deep …
important basis for the management of the photovoltaic power generation system. Deep …