Deep-learning-based approaches for semantic segmentation of natural scene images: A review
The task of semantic segmentation holds a fundamental position in the field of computer
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …
MIC: Masked image consistency for context-enhanced domain adaptation
In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …
adapted to target data (eg real-world) without access to target annotation. Most previous …
Spherical transformer for lidar-based 3d recognition
LiDAR-based 3D point cloud recognition has benefited various applications. Without
specially considering the LiDAR point distribution, most current methods suffer from …
specially considering the LiDAR point distribution, most current methods suffer from …
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 …
Diffusion-based image translation with label guidance for domain adaptive semantic segmentation
Translating images from a source domain to a target domain for learning target models is
one of the most common strategies in domain adaptive semantic segmentation (DASS) …
one of the most common strategies in domain adaptive semantic segmentation (DASS) …
Dnf: Decouple and feedback network for seeing in the dark
The exclusive properties of RAW data have shown great potential for low-light image
enhancement. Nevertheless, the performance is bottlenecked by the inherent limitations of …
enhancement. Nevertheless, the performance is bottlenecked by the inherent limitations of …
HD-Net: High-resolution decoupled network for building footprint extraction via deeply supervised body and boundary decomposition
The extraction of building footprints, as a highly challenging task in remote sensing (RS)
image-based geospatial object detection and recognition, holds significant importance. Due …
image-based geospatial object detection and recognition, holds significant importance. Due …
To adapt or not to adapt? real-time adaptation for semantic segmentation
MB Colomer, PL Dovesi… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract The goal of Online Domain Adaptation for semantic segmentation is to handle
unforeseeable domain changes that occur during deployment, like sudden weather events …
unforeseeable domain changes that occur during deployment, like sudden weather events …
Domain adaptive and generalizable network architectures and training strategies for semantic image segmentation
Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine
learning models trained on a source domain to perform well on unlabeled or even unseen …
learning models trained on a source domain to perform well on unlabeled or even unseen …
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 …