L Hoyer, D Dai, H Wang… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
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 …
X Lai, Y Chen, F Lu, J Liu, J Jia - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from …
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 …
D Peng, P Hu, Q Ke, J Liu - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
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) …
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 …
Y Li, D Hong, C Li, J Yao, J Chanussot - ISPRS Journal of Photogrammetry …, 2024 - Elsevier
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 …
Abstract The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events …
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 …
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 …