Zobrazeno 1 - 10
of 788
pro vyhledávání: '"He, Zhiqiang"'
Autor:
Hou, Feng, Yuan, Jin, Yang, Ying, Liu, Yang, Zhang, Yang, Zhong, Cheng, Shi, Zhongchao, Fan, Jianping, Rui, Yong, He, Zhiqiang
Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-language models (VLMs), viewed as natural source models, the cross-domain t
Externí odkaz:
http://arxiv.org/abs/2403.02714
Autor:
Shao, Fang, He, Zhiqiang, Zhu, Zheng, Wang, Xiang, Zhang, Jianping, Shan, Jinhua, Pan, Jiajia, Wang, Hui
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 6, p e17997 (2020)
BackgroundThe prevalence of infertility in China is high, but the advent of assisted reproduction technology (ART) has greatly eased this situation. Social media, such as WeChat official accounts, have become the preferred tool for ART centers to com
Externí odkaz:
https://doaj.org/article/434697909c584f22824d8173aa52aaa2
Publikováno v:
Industrial Lubrication and Tribology, 2024, Vol. 76, Issue 10, pp. 1197-1204.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ILT-05-2024-0192
Designing versatile graph learning approaches is important, considering the diverse graphs and tasks existing in real-world applications. Existing methods have attempted to achieve this target through automated machine learning techniques, pre-traini
Externí odkaz:
http://arxiv.org/abs/2309.04565
In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are sub-optimal without utilizing
Externí odkaz:
http://arxiv.org/abs/2302.08671
In recent years, Graph Neural Networks (GNNs) have been popular in graph representation learning which assumes the homophily property, i.e., the connected nodes have the same label or have similar features. However, they may fail to generalize into t
Externí odkaz:
http://arxiv.org/abs/2211.10990
Autor:
Hou, Feng, Zhang, Yao, Liu, Yang, Yuan, Jin, Zhong, Cheng, Zhang, Yang, Shi, Zhongchao, Fan, Jianping, He, Zhiqiang
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice. Domain generalization (DG) aims to overcome this issue by capturing domain-invariant representations from source domains. Motivated by
Externí odkaz:
http://arxiv.org/abs/2211.04582
Autor:
Liu, Yang, Zhang, Yao, Wang, Yixin, Zhang, Yang, Tian, Jiang, Shi, Zhongchao, Fan, Jianping, He, Zhiqiang
Recently, the dominant DETR-based approaches apply central-concept spatial prior to accelerate Transformer detector convergency. These methods gradually refine the reference points to the center of target objects and imbue object queries with the upd
Externí odkaz:
http://arxiv.org/abs/2211.02006
Occluded person re-identification (Re-ID) aims at addressing the occlusion problem when retrieving the person of interest across multiple cameras. With the promotion of deep learning technology and the increasing demand for intelligent video surveill
Externí odkaz:
http://arxiv.org/abs/2207.14452
Intestinal parasitic infections, as a leading causes of morbidity worldwide, still lacks time-saving, high-sensitivity and user-friendly examination method. The development of deep learning technique reveals its broad application potential in biologi
Externí odkaz:
http://arxiv.org/abs/2207.01419