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pro vyhledávání: '"WANG, Shiping"'
Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness ch
Externí odkaz:
http://arxiv.org/abs/2412.12596
Autor:
Chen, Yuhong, Song, Ailin, Yin, Huifeng, Zhong, Shuai, Chen, Fuhai, Xu, Qi, Wang, Shiping, Xu, Mingkun
The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating
Externí odkaz:
http://arxiv.org/abs/2412.12801
Autor:
Chen, Zhaoliang, Wu, Zhihao, Sadikaj, Ylli, Plant, Claudia, Dai, Hong-Ning, Wang, Shiping, Cheung, Yiu-Ming, Guo, Wenzhong
Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile rob
Externí odkaz:
http://arxiv.org/abs/2403.09171
Graphs with abundant attributes are essential in modeling interconnected entities and improving predictions in various real-world applications. Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to
Externí odkaz:
http://arxiv.org/abs/2403.04780
Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasi
Externí odkaz:
http://arxiv.org/abs/2401.03459
Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose Graph Con
Externí odkaz:
http://arxiv.org/abs/2312.15971
Autor:
Du, Shide, Fang, Zihan, Lan, Shiyang, Tan, Yanchao, Günther, Manuel, Wang, Shiping, Guo, Wenzhong
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has bec
Externí odkaz:
http://arxiv.org/abs/2308.03666
The current work experimentally studies the complex interaction between underwater explosion (UNDEX) bubbles and a free surface. We aim to reveal the dependence of the associated physics on the key factor, namely, the dimensionless detonation depth $
Externí odkaz:
http://arxiv.org/abs/2306.14720
Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to w
Externí odkaz:
http://arxiv.org/abs/2304.07014
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the qu
Externí odkaz:
http://arxiv.org/abs/2304.06336