Zobrazeno 1 - 10
of 13 045
pro vyhledávání: '"Domain-shift"'
Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation
Externí odkaz:
http://arxiv.org/abs/2412.15601
Recent advancements in anomaly detection have shifted focus towards Multi-class Unified Anomaly Detection (MUAD), offering more scalable and practical alternatives compared to traditional one-class-one-model approaches. However, existing MUAD methods
Externí odkaz:
http://arxiv.org/abs/2411.16049
Autor:
Ridge, Jeremiah, Jones, Oiwi Parker
Machine learning techniques have enabled researchers to leverage neuroimaging data to decode speech from brain activity, with some amazing recent successes achieved by applications built using invasive devices. However, research requiring surgical im
Externí odkaz:
http://arxiv.org/abs/2410.19986
Autor:
Yuan, Haohan, Zhang, Haopeng
Most research on abstractive summarization focuses on single-domain applications, often neglecting how domain shifts between documents affect performance and the generalization ability of summarization models. To address this issue, we introduce Doma
Externí odkaz:
http://arxiv.org/abs/2410.15687
Guidewire segmentation during endovascular interventions holds the potential to significantly enhance procedural accuracy, improving visualization and providing critical feedback that can support both physicians and robotic systems in navigating comp
Externí odkaz:
http://arxiv.org/abs/2410.07460
Autor:
Cui, Qinpeng, Liu, Yixuan, Zhang, Xinyi, Bao, Qiqi, Liao, Qingmin, Wang, Li, Lu, Tian, Liu, Zicheng, Wang, Zhongdao, Barsoum, Emad
Diffusion-based image super-resolution (SR) models have attracted substantial interest due to their powerful image restoration capabilities. However, prevailing diffusion models often struggle to strike an optimal balance between efficiency and perfo
Externí odkaz:
http://arxiv.org/abs/2409.17778
Autor:
Guo, Brian, Lu, Darui, Szumel, Gregory, Gui, Rongze, Wang, Tingyu, Konz, Nicholas, Mazurowski, Maciej A.
Purpose: Medical images acquired using different scanners and protocols can differ substantially in their appearance. This phenomenon, scanner domain shift, can result in a drop in the performance of deep neural networks which are trained on data acq
Externí odkaz:
http://arxiv.org/abs/2409.04368
Transformer-based methods have achieved remarkable success in various machine learning tasks. How to design efficient test-time adaptation methods for transformer models becomes an important research task. In this work, motivated by the dual-subband
Externí odkaz:
http://arxiv.org/abs/2408.13983
The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar unde
Externí odkaz:
http://arxiv.org/abs/2408.06772
Autor:
Chandrashekar, Mayanka, Goethert, Ian, Haque, Md Inzamam Ul, McMahon, Benjamin, Dhaubhadel, Sayera, Knight, Kathryn, Erdos, Joseph, Reagan, Donna, Taylor, Caroline, Kuzmak, Peter, Gaziano, John Michael, McAllister, Eileen, Costa, Lauren, Ho, Yuk-Lam, Cho, Kelly, Tamang, Suzanne, Fodeh-Jarad, Samah, Ovchinnikova, Olga S., Justice, Amy C., Hinkle, Jacob, Danciu, Ioana
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials and Methods:
Externí odkaz:
http://arxiv.org/abs/2407.21149