Zobrazeno 1 - 10
of 6 766
pro vyhledávání: '"domain alignment"'
Autor:
Wu, Jiahua1 (AUTHOR) jhwu@shu.edu.cn, Fang, Yuchun1 (AUTHOR) ycfang@shu.edu.cn
Publikováno v:
Symmetry (20738994). Sep2024, Vol. 16 Issue 9, p1216. 20p.
Autor:
Wyatt, Julian, Voiculescu, Irina
Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. Thi
Externí odkaz:
http://arxiv.org/abs/2410.04445
Unsupervised Domain Adaptation (UDA) is crucial for reducing the need for extensive manual data annotation when training deep networks on point cloud data. A significant challenge of UDA lies in effectively bridging the domain gap. To tackle this cha
Externí odkaz:
http://arxiv.org/abs/2410.02720
Autor:
Guo, Jiayi, Zhao, Junhao, Ge, Chunjiang, Du, Chaoqun, Ni, Zanlin, Song, Shiji, Shi, Humphrey, Huang, Gao
Test-time adaptation (TTA) aims to enhance the performance of source-domain pretrained models when tested on unknown shifted target domains. Traditional TTA methods primarily adapt model weights based on target data streams, making model performance
Externí odkaz:
http://arxiv.org/abs/2406.04295
Cross-domain alignment refers to the task of mapping a concept from one domain to another. For example, ``If a \textit{doctor} were a \textit{color}, what color would it be?''. This seemingly peculiar task is designed to investigate how people repres
Externí odkaz:
http://arxiv.org/abs/2405.14863
Autor:
Yuan, Chaohao, Li, Songyou, Ye, Geyan, Zhang, Yikun, Huang, Long-Kai, Huang, Wenbing, Liu, Wei, Yao, Jianhua, Rong, Yu
The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions. Current models explore to generate protein using structural and evolutionary guidance, which only provide indi
Externí odkaz:
http://arxiv.org/abs/2404.16866
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques. Recently,
Externí odkaz:
http://arxiv.org/abs/2401.05465
Autor:
Kong, Zhe, Zhang, Wentian, Wang, Tao, Zhang, Kaihao, Li, Yuexiang, Tang, Xiaoying, Luo, Wenhan
Face recognition systems have raised concerns due to their vulnerability to different presentation attacks, and system security has become an increasingly critical concern. Although many face anti-spoofing (FAS) methods perform well in intra-dataset
Externí odkaz:
http://arxiv.org/abs/2401.01102
This paper reports the impact of process-dependent structural deformation and lattice strain by doping, resulting in domain re-orientation along the a-axis. For this investigation, the smaller La3+ cation is introduced at A-site and the longitudinal
Externí odkaz:
http://arxiv.org/abs/2312.07410
Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from t
Externí odkaz:
http://arxiv.org/abs/2401.05363