Results 91 to 100 of about 2,905 (180)

RAAN: A Gaussian Prior Domain Adaptive Network for Rolling Bearing Fault Diagnosis Under Variable Working Conditions

Complex System Modeling and Simulation
In the field of fault diagnosis for rolling bearings under variable working conditions, significant progress has been made using methods based on unsupervised domain adaptation (UDA). However, most existing UDA methods primarily achieve identification by
Kang Liu, Yang Yu, Yuanjiang Li, Tao Lang, Ruochen Liu   +4 more
doaj   +1 more source

VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited data. In parallel, Vision Foundation Models (VFMs) pretrained at scale without labels have also shown impressive downstream performance and generalization.
Englert, Brunó B., Dubbelman, Gijs
openaire   +2 more sources

Enhancing the Detection of Coastal Marine Debris in Very High-Resolution Satellite Imagery via Unsupervised Domain Adaptation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In this study, we propose a robust debris estimation model applied to satellite imagery that is suitable for practical applications. In our previous study, we proposed a coastal marine debris estimation model using semantic segmentation applied to very ...
Kenichi Sasaki, Tatsuyuki Sekine, William Emery   +2 more
doaj   +1 more source

TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation

Unsupervised domain adaptation for medical image segmentation remains a significant challenge due to substantial domain shifts across imaging modalities, such as CT and MRI. While recent vision-language representation learning methods have shown promise, their potential in UDA segmentation tasks remains underexplored.
Maurya, Lalit, Liu, Honghai, Zwiggelaar, Reyer   +2 more
openaire   +2 more sources

Multi-View Prototypical Transport for Unsupervised Domain Adaptation

IEEE Access
Unsupervised Domain Adaptation (UDA) methods struggle to bridge the gap between a labeled source domain and an unlabeled target domain, particularly due to the rigidity of deep feature representations derived from the penultimate layer of backbone ...
Sunhyeok Lee, Dae-Shik Kim
doaj   +1 more source

Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models

The scarcity and complexity of voxel-level annotations in 3D medical imaging present significant challenges, particularly due to the domain gap between labeled datasets from well-resourced centers and unlabeled datasets from less-resourced centers. This disparity affects the fairness of artificial intelligence algorithms in healthcare.
Gong, Haifan, Wang, Yitao, Wang, Yihan, Xiao, Jiashun, Wan, Xiang, Li, Haofeng   +5 more
openaire   +2 more sources

Cross-Domain Land Cover Classification of Remote Sensing Images Based on Full-Level Domain Adaptation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The use of remote sensing images for land cover classification is an important and challenging pixel-level classification task. However, the different distribution of the same land cover categories across different datasets, the accuracy of the ...
Yuke Meng, Zhanliang Yuan, Jian Yang, Peizhuo Liu, Jian Yan, Hongbo Zhu, Zhigao Ma, Zhenzhao Jiang, Zhouwei Zhang, Xiaofei Mi   +9 more
doaj   +1 more source

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation

Segmentation models are typically constrained by the categories defined during training. To address this, researchers have explored two independent approaches: adapting Vision-Language Models (VLMs) and leveraging synthetic data. However, VLMs often struggle with granularity, failing to disentangle fine-grained concepts, while synthetic data-based ...
Alcover-Couso, Roberto, Escudero-Viñolo, Marcos, SanMiguel, Juan C., Bescos, Jesus   +3 more
openaire   +1 more source

Learning Domain-Invariant Representations for Event-Based Motion Segmentation: An Unsupervised Domain Adaptation Approach. [PDF]

J Imaging
Jeryo M, Harati A.
europepmc   +1 more source

ADAM-Net: Anatomy-Guided Attentive Unsupervised Domain Adaptation for Joint MG Segmentation and MGD Grading. [PDF]

J Imaging
Fang J, He X, Jiang Y, Wang MH.
europepmc   +1 more source