Along with the popularity and development of the Internet in China, Chinese webpage classification has become an important research topic. As the webpage text is a kind of text, webpage classification is constructed b...
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ISBN:
(数字)9789819723874
ISBN:
(纸本)9789819723867;9789819723874
Along with the popularity and development of the Internet in China, Chinese webpage classification has become an important research topic. As the webpage text is a kind of text, webpage classification is constructed based on text classification. But due the particularity of the webpage composition, the external linked webpages can leverage helpful information to improve the webpage classification performance. The goal of this work is to design accurate multi-label Chinese webpage classification models by effectively fusing the information extracted from current webpage and external linked webpages, including the text information and label information of external linked webpages. A convolutional neural network for webpage classification (PageCNN) model and its two variants (PageCNN-CLL and PageCNN-WLL) are proposed to effectively fuse the text and label information extracted from multiple Chinese webpages. The proposed PageCNN models are compared with two modified traditional machine learning models, the modified TextCNN model, and three state-of-the-art deep learning based multi-label text classification models. The experimental results demonstrate that the PageCNN models perform better than the compared models in terms of subset accuracy, Hamming loss, macro F1, and micro F1. Moreover, the in-depth analysis of the effectiveness of the external linked webpages on current webpage classification is conducted by analyzing the error correction rate and hit rate of the proposed models and preliminary prediction variables. As demonstrated in the experiments, the multi-information fusion methods developed in the PageCNN models can effectively manipulate the input data from multiple webpages to enhance the multi-label Chinese webpage classification performance.
Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing, with applications ranging from virtual try-on, movies, to games. To enable practical and widespread applications, recen...
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ISBN:
(纸本)9798350353006
Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing, with applications ranging from virtual try-on, movies, to games. To enable practical and widespread applications, recent advances propose to generate a clothed human from an RGB image. However, they struggle to reconstruct detailed and robust avatars simultaneously. We empirically find that the high-frequency (HF) and low-frequency (LF) information from a parametric model has the potential to enhance geometry details and improve robustness to noise, respectively. Based on this, we propose HiLo, namely clothed human reconstruction with high- and low-frequency information, which contains two components. 1) To recover detailed geometry using HF information, we propose a progressive HF Signed Distance Function to enhance the detailed 3D geometry of a clothed human. We analyze that our progressive learning manner alleviates large gradients that hinder model convergence. 2) To achieve robust reconstruction against inaccurate estimation of the parametric model by using LF information, we propose a spatial interaction implicit function. This function effectively exploits the complementary spatial information from a low-resolution voxel grid of the parametric model. Experimental results demonstrate that HiLo outperforms the state-of-the-art methods by 10.43% and 9.54% in terms of Chamfer distance on the Thuman2.0 and CAPE datasets, respectively. Additionally, HiLo demonstrates robustness to noise from the parametric model, challenging poses, and various clothing styles.(1)
As a new type of biometrics recognition technology, speaker recognition is gaining more and more attention because of the advantages in remote authentication. In this paper, we construct an end-to-end speaker recognit...
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Spodoptera frugiperda (Fall armyworm, FAW) is one of the major agricultural pests in the world. It is of great significance for ensuring crop yield to accurately recognize FAW larval instars. This research proposes a ...
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Developing a personalized insulin regimen for the treatment of Type 1 Diabetes (T1D) is a healthcare task with very high safety requirements. Compared to traditional control algorithms, current reinforcement learning-...
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In accelerated MRI reconstruction problem, directly recovering all the missing k-space data from undersampled measurements is highly ill-posed and often leads to suboptimal performance. To address the problem, we prop...
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Model-based networks have shown convincing performance in MRI reconstruction. However, the unrolled cascades within the networks are constrained to solely obtain information from the preceding counterpart, resulting i...
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Syslog records critical information of network when the system is running, and has been used to help practitioners carry out various network maintenance and operation activities. Because of abundance of syslog, automa...
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In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power using extremely limited annotated dat...
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Deep unfolding networks (DUNs) have made significant progress in MRI reconstruction, successfully tackling the problem of prolonged imaging time. However, the ill-conditioned nature of MRI reconstruction often causes ...
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