Zobrazeno 1 - 10
of 895
pro vyhledávání: '"ZHENG Qinghua"'
Publikováno v:
中国工程科学, Vol 25, Iss 2, Pp 221-231 (2023)
Taxation is vital for national governance, and the digital transformation of governments necessitates smart taxation. Therefore, analyzing the key issues and exploring the development ideas for smart taxation is of both theoretical and practical valu
Externí odkaz:
https://doaj.org/article/860b734f29b843fe9fc235c335ce3f71
Publikováno v:
中国工程科学, Vol 25, Iss 2, Pp 208-220 (2023)
Big Data Knowledge Engineering is the infrastructure of artificial intelligence, a common requirement faced by various industries and fields, and the inevitable path for the digitalization to intelligence. In this paper, we firstly elaborate on the b
Externí odkaz:
https://doaj.org/article/44041f55550b4b22a0ce00003992c682
The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It construct
Externí odkaz:
http://arxiv.org/abs/2412.14451
Autor:
Meng, Shiqiao, Zhou, Ying, Zheng, Qinghua, Liao, Bingxu, Chang, Mushi, Zhang, Tianshu, Djerrad, Abderrahim
Accurately predicting the dynamic responses of building structures under seismic loads is essential for ensuring structural safety and minimizing potential damage. This critical aspect of structural analysis allows engineers to evaluate how structure
Externí odkaz:
http://arxiv.org/abs/2410.20186
Collecting well-matched multimedia datasets is crucial for training cross-modal retrieval models. However, in real-world scenarios, massive multimodal data are harvested from the Internet, which inevitably contains Partially Mismatched Pairs (PMPs).
Externí odkaz:
http://arxiv.org/abs/2403.05105
We study the challenging problem for inference tasks on large-scale graph datasets of Graph Neural Networks: huge time and memory consumption, and try to overcome it by reducing reliance on graph structure. Even though distilling graph knowledge to s
Externí odkaz:
http://arxiv.org/abs/2403.01079
New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering structure relationships between samples, previous methods generate noi
Externí odkaz:
http://arxiv.org/abs/2310.15836
Discovering fine-grained categories from coarsely labeled data is a practical and challenging task, which can bridge the gap between the demand for fine-grained analysis and the high annotation cost. Previous works mainly focus on instance-level disc
Externí odkaz:
http://arxiv.org/abs/2310.10151
Autor:
Zhang, Zhihao, Chen, Yiwei, Zhang, Weizhan, Yan, Caixia, Zheng, Qinghua, Wang, Qi, Chen, Wangdu
Viewport prediction is a crucial aspect of tile-based 360 video streaming system. However, existing trajectory based methods lack of robustness, also oversimplify the process of information construction and fusion between different modality inputs, l
Externí odkaz:
http://arxiv.org/abs/2309.14704
Point cloud analysis (such as 3D segmentation and detection) is a challenging task, because of not only the irregular geometries of many millions of unordered points, but also the great variations caused by depth, viewpoint, occlusion, etc. Current s
Externí odkaz:
http://arxiv.org/abs/2307.14605