Zobrazeno 1 - 10
of 1 345
pro vyhledávání: '"Liu Yipeng"'
This paper proposes fast randomized algorithms for computing the Kronecker Tensor Decomposition (KTD). The proposed algorithms can decompose a given tensor into the KTD format much faster than the existing state-of-the-art algorithms. Our principal i
Externí odkaz:
http://arxiv.org/abs/2412.02597
No-Reference Point Cloud Quality Assessment (NR-PCQA) aims to objectively assess the human perceptual quality of point clouds without relying on pristine-quality point clouds for reference. It is becoming increasingly significant with the rapid advan
Externí odkaz:
http://arxiv.org/abs/2411.07936
Glaucoma is a leading cause of irreversible blindness worldwide. While deep learning approaches using fundus images have largely improved early diagnosis of glaucoma, variations in images from different devices and locations (known as domain shifts)
Externí odkaz:
http://arxiv.org/abs/2407.04396
Additive models can be used for interpretable machine learning for their clarity and simplicity. However, In the classical models for high-order data, the vectorization operation disrupts the data structure, which may lead to degenerated accuracy and
Externí odkaz:
http://arxiv.org/abs/2406.02980
Autor:
Wu, Ruituo, Chen, Yang, Xiao, Jian, Li, Bing, Fan, Jicong, Dufaux, Frédéric, Zhu, Ce, Liu, Yipeng
Cooperation between temporal convolutional networks (TCN) and graph convolutional networks (GCN) as a processing module has shown promising results in skeleton-based video anomaly detection (SVAD). However, to maintain a lightweight model with low co
Externí odkaz:
http://arxiv.org/abs/2406.02976
Implicit neural representations (INR) suffer from worsening spectral bias, which results in overly smooth solutions to the inverse problem. To deal with this problem, we propose a universal framework for processing inverse problems called \textbf{Hig
Externí odkaz:
http://arxiv.org/abs/2404.14674
Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the consensus embedding feature for clustering by exploring global correlatio
Externí odkaz:
http://arxiv.org/abs/2403.09107
In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR). However, both HSI and MSI may be
Externí odkaz:
http://arxiv.org/abs/2403.09096
Second-order optimization techniques have the potential to achieve faster convergence rates compared to first-order methods through the incorporation of second-order derivatives or statistics. However, their utilization in deep learning is limited du
Externí odkaz:
http://arxiv.org/abs/2403.03473
Coupled tensor decomposition (CTD) can extract joint features from multimodal data in various applications. It can be employed for federated learning networks with data confidentiality. Federated CTD achieves data privacy protection by sharing common
Externí odkaz:
http://arxiv.org/abs/2403.02898