Results 21 to 30 of about 3,036,262 (193)
Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning [PDF]
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 Accepted to WACV ...
Vibashan VS +4 more
openaire +2 more sources
A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation [PDF]
, 2022 We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality medical image segmentation, aiming to perform segmentation on the unannotated target domain (e.g. MRI) with the help of labeled source domain (e.g.
Hussain, Amir +6 more
core +1 more source
Sensors, 2023 Unsupervised domain adaptation (UDA) aims to mitigate the performance drop due to the distribution shift between the training and testing datasets. UDA methods have achieved performance gains for models trained on a source domain with labeled data to a ...
Rajat Sahay +5 more
doaj +1 more source
LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 We introduce LiDAR-UDA, a novel two-stage self-training-based Unsupervised Domain Adaptation (UDA) method for LiDAR segmentation. Existing self-training methods use a model trained on labeled source data to generate pseudo labels for target data and refine the predictions via fine-tuning the network on the pseudo labels.
Amirreza Shaban +4 more
openaire +3 more sources
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Accepted to CVPR ...
Taeyeop Lee +6 more
openaire +3 more sources
Cross Domain Mean Approximation for Unsupervised Domain Adaptation
IEEE Access, 2020 Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled source domain to help the task of target domain with the unlabeled data. It is a key step for UDA to minimize the cross-domain distribution divergence. In this paper, we
Shaofei Zang +4 more
doaj +1 more source
International Journal of Applied Earth Observations and Geoinformation, 2022 Despite its success, deep learning in land cover mapping requires a massive amount of pixel-wise labeled images. It typically assumes that the training and test scenes are similar in data distribution.
Wei Liu +5 more
doaj +1 more source
O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation
Knowledge-Based Systems, 2023 One-to-multiple medical image segmentation aims to directly test a segmentation model trained with the medical images of a one-domain site on those of a multiple-domain site, suffering from segmentation performance degradation on multiple domains. This process avoids additional annotations and helps improve the application value of the model.
Ziyue Jiang 0004 +9 more
openaire +3 more sources
Animals, 2023 Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior.
Seong-Ho Ahn, Seeun Kim, Dong-Hwa Jeong
doaj +1 more source
Unsupervised domain adaptation for lip reading based on cross-modal knowledge distillation
EURASIP Journal on Audio, Speech, and Music Processing, 2021 We present an unsupervised domain adaptation (UDA) method for a lip-reading model that is an image-based speech recognition model. Most of conventional UDA methods cannot be applied when the adaptation data consists of an unknown class, such as out-of ...
Yuki Takashima +6 more
doaj +1 more source