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pro vyhledávání: '"Zhao Haifeng"'
Medical images from different healthcare centers exhibit varied data distributions, posing significant challenges for adapting lung nodule detection due to the domain shift between training and application phases. Traditional unsupervised domain adap
Externí odkaz:
http://arxiv.org/abs/2407.19397
Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with missing la
Externí odkaz:
http://arxiv.org/abs/2407.18520
The label annotations for chest X-ray image rib segmentation are time consuming and laborious, and the labeling quality heavily relies on medical knowledge of annotators. To reduce the dependency on annotated data, existing works often utilize genera
Externí odkaz:
http://arxiv.org/abs/2407.15903
Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling la
Externí odkaz:
http://arxiv.org/abs/2307.09715
Autor:
Seneviratne, Sachith, Wijnands, Jasper S., Nice, Kerry, Zhao, Haifeng, Godic, Branislava, Mavoa, Suzanne, Vidanaarachchi, Rajith, Stevenson, Mark, Garcia, Leandro, Hunter, Ruth F., Thompson, Jason
Analysis of overhead imagery using computer vision is a problem that has received considerable attention in academic literature. Most techniques that operate in this space are both highly specialised and require expensive manual annotation of large d
Externí odkaz:
http://arxiv.org/abs/2208.08047
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples that are crafted with imperceptible perturbations, i.e., a small change in an input image can induce a mis-classification, and thus threatens the reliability of deep learn
Externí odkaz:
http://arxiv.org/abs/2207.13036
Autor:
Zhan, Fei, Zheng, Lirong, Yao, Haodong, Geng, Zhi, Yu, Can, Han, Xue, Song, Xueqi, Chen, Shuguang, Zhao, Haifeng
X-ray absorption spectroscopy (XAS) is an indispensable tool to characterize the atomic-scale three-dimensional local structure of the system, in which XANES is the most important energy region to reflect the three-dimensional structure. However quan
Externí odkaz:
http://arxiv.org/abs/2205.04463
In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap between the two image domains with different view-points. Instead of learnin
Externí odkaz:
http://arxiv.org/abs/2203.11832
Autor:
Ding, Cancan, Zhao, Haifeng, Hu, Bin, Xu, Dechao, Ge, Ru, Deng, Chengyuan, Xie, Zedong, Chen, Hua, Luo, Haiwen
Publikováno v:
In Journal of Materials Science & Technology 10 October 2024 196:171-182
Autor:
Zhao, Haifeng, Khaliq, Nosherwan
Publikováno v:
In Technological Forecasting & Social Change October 2024 207