Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
clinical approaches. Recent success of deep learning-based segmentation methods usually …
Depth anything: Unleashing the power of large-scale unlabeled data
Abstract This work presents Depth Anything a highly practical solution for robust monocular
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …
Conflict-based cross-view consistency for semi-supervised semantic segmentation
Semi-supervised semantic segmentation (SSS) has recently gained increasing research
interest as it can reduce the requirement for large-scale fully-annotated training data. The …
interest as it can reduce the requirement for large-scale fully-annotated training data. The …
Switching temporary teachers for semi-supervised semantic segmentation
The teacher-student framework, prevalent in semi-supervised semantic segmentation,
mainly employs the exponential moving average (EMA) to update a single teacher's weights …
mainly employs the exponential moving average (EMA) to update a single teacher's weights …
Advances and challenges in deep learning-based change detection for remote sensing images: A review through various learning paradigms
Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting
changes in the Earth's surface, finding wide applications in urban planning, disaster …
changes in the Earth's surface, finding wide applications in urban planning, disaster …
DAW: exploring the better weighting function for semi-supervised semantic segmentation
The critical challenge of semi-supervised semantic segmentation lies in how to fully exploit a
large volume of unlabeled data to improve the model's generalization performance for …
large volume of unlabeled data to improve the model's generalization performance for …
SemiVL: semi-supervised semantic segmentation with vision-language guidance
In semi-supervised semantic segmentation, a model is trained with a limited number of
labeled images along with a large corpus of unlabeled images to reduce the high annotation …
labeled images along with a large corpus of unlabeled images to reduce the high annotation …
Adapting segment anything model for change detection in VHR remote sensing images
Vision foundation models (VFMs), such as the segment anything model (SAM), allow zero-
shot or interactive segmentation of visual contents; thus, they are quickly applied in a variety …
shot or interactive segmentation of visual contents; thus, they are quickly applied in a variety …
Training Vision Transformers for Semi-Supervised Semantic Segmentation
We present S4Former a novel approach to training Vision Transformers for Semi-Supervised
Semantic Segmentation (S4). At its core S4Former employs a Vision Transformer within a …
Semantic Segmentation (S4). At its core S4Former employs a Vision Transformer within a …
Unimatch v2: Pushing the limit of semi-supervised semantic segmentation
Semi-supervised semantic segmentation (SSS) aims at learning rich visual knowledge from
cheap unlabeled images to enhance semantic segmentation capability. Among recent …
cheap unlabeled images to enhance semantic segmentation capability. Among recent …