检索结果-内蒙古大学图书馆

16th European Conference on Computer Vision, ECCV 2020

作者： Zhu, Heming Cao, Yu Jin, Hang Chen, Weikai Du, Dong Wang, Zhangye Cui, Shuguang Han, Xiaoguang Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China State Key Lab of CAD&CG Zhejiang University Hangzhou China Xidian University Xi’an China Tencent America Palo Alto United States University of Science and Technology of China Hefei China

ISBN: (纸本)9783030584511

High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc. Recent advances in learning-based approaches have accomplished unprecedented accuracy in recovering unclothed human shape and pose from single images, thanks to the availability of powerful statistical models, e.g. SMPL, learned from a large number of body scans. In contrast, modeling and recovering clothed human and 3D garments remains notoriously difficult, mostly due to the lack of large-scale clothing models available for the research community. We propose to fill this gap by introducing Deep Fashion3D, the largest collection to date of 3D garment models, with the goal of establishing a novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. In addition, each garment is randomly posed to enhance the variety of real clothing deformations. To demonstrate the advantage of Deep Fashion3D, we propose a novel baseline approach for single-view garment reconstruction, which leverages the merits of both mesh and implicit representations. A novel adaptable template is proposed to enable the learning of all types of clothing in a single network. Extensive experiments have been conducted on the proposed dataset to verify its significance and usefulness. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

A Pose Measurement Method of Non-cooperative Target Based on Monocular Vision

A Pose Measurement Method of Non-cooperative Target Based on...

引用

第33届中国控制与决策会议

作者： Chenrong Long Zhining Bai Shuai Zhi Chengbo Qiu Yamin Wang Qinglei Hu School of Automation Science and Electrical Engineering Beihang University Beijing Institute of Space Mechanics and Electricity Key lab of Microsatellite Chinese Academy of Sciences Innovation Academy for Microsatellites of CAS Beijing Advanced Innovation Center for Big-Data Based Precision Medicine Beihang University

A method based on monocular vision is presented in this paper to estimate the pose of the non-cooperative target *** directly takes the image features of the docking ring and solar panel as the recognized *** circular feature is used to restore two solutions of the relative position and orientation,the vanishing points formed by the two parallel lines of the solar panel are applied to solve the duality problem and restore the roll *** algorithm provides a closed-form solution using simple mathematics,therefore,it is suitable to space applications where the computation capability of the on-board processor is very *** simulations show that the proposed algorithm can effectively estimate the pose of the non-cooperative target spacecraft.

关键词： Non-cooperative target image features docking ring solar panel

来源：评论

学校读者我要写书评

暂无评论

KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

引用

arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

来源：评论

学校读者我要写书评

暂无评论

Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

引用

Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

来源：评论

学校读者我要写书评

暂无评论

CKG: Dynamic Representation Based on Context and Knowledge Graph

CKG: Dynamic Representation Based on Context and Knowledge G...

引用

International Conference on Pattern Recognition

作者： Xunzhu Tang Tiezhu Sun Rujie Zhu Shi Wang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Momenta Suzhou China University of Central Florida Orlando FL USA Institute of Computing Technology Chinese Academy beijing China

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on Context and Knowledge Graph. On the one side, CKG can extract rich semantic information of large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. The experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and BERTBase (88.5).

关键词： Semantics data mining Task analysis Context modeling

来源：评论

学校读者我要写书评

暂无评论

GraphWGAN: Graph Representation Learning with Wasserstein Generative Adversarial Networks

GraphWGAN: Graph Representation Learning with Wasserstein Ge...

引用

International Conference on big data and Smart Computing (bigCOMP)

作者： Rong Yan Huawei Shen Cao Qi Keting Cen Li Wang College of Big Data Taiyuan University Of Technology Taiyuan China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Beijing China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distance between the true connectivity distribution and generated connectivity distribution by Kullback-Leibler or Jensen-Shannon divergence. However, since these divergences are not continuous with respect to the generator's parameters, such methods easily lead to unstable training and poor convergence. In contrast, Wasserstein distance is continuous and differentiable almost everywhere, which means it can produce more reliable gradient, allowing the training more stable and more convergent. In this paper, we utilize Wasserstein distance to characterize the distance between the underlying true connectivity distribution and generated distribution in graph representation learning. Experimental results show that the accuracy of our method exceeds existing baselines in tasks of both node classification and link prediction.

关键词： Hafnium Conferences big data

来源：评论

学校读者我要写书评

暂无评论

Improving visualization interpretation using counterfactuals

arXiv

引用

arXiv 2021年

作者： Kaul, Smiti Borland, David Cao, Nan Gotz, David Dept. of Computer Science University of North Carolina at Chapel Hill United States RENCI University of North Carolina at Chapel Hill United States Intelligent Big Data Visualization Lab Tongji University China School of Information and Library Science University of North Carolina at Chapel Hill United States

Complex, high-dimensional data is used in a wide range of domains to explore problems and make decisions. Analysis of high-dimensional data, however, is vulnerable to the hidden influence of confounding variables, especially as users apply ad hoc filtering operations to visualize only specific subsets of an entire dataset. Thus, visual data-driven analysis can mislead users and encourage mistaken assumptions about causality or the strength of relationships between features. This work introduces a novel visual approach designed to reveal the presence of confounding variables via counterfactual possibilities during visual data analysis. It is implemented in CoFact, an interactive visualization prototype that determines and visualizes counterfactual subsets to better support user exploration of feature relationships. Using publicly available datasets, we conducted a controlled user study to demonstrate the effectiveness of our approach;the results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships. Copyright © 2021, The Authors. All rights reserved.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Elastic-plastic time-history analysis of steel-tube stiff-frame arch bridge without inner filling concrete 3

Elastic-plastic time-history analysis of steel-tube stiff-fr...

引用

2020 3rd International Symposium on Traffic Transportation and Civil Architecture, ISTTCA 2020

作者： Zhang, Ben Li, Li Su, Wenming School of Civil Engineering Chongqing Jiaotong University Chongqing400074 China China Railway Bridge Science Research Institute Ltd. Wuhan430034 China State Key Laborary for Health and Safety of Bridge Structures Wuhan430034 China Shandong Hi-Speed Engineering Testing Co. Ltd Jinan250000 China Key Lab. for Bridge Struct. Big Data Perf. Diagn. and Treatm. Improvement of Transp. Industry Jinan250000 China

Based on a case study of a typical arch bridge with hollow steel tube and stiffness frame located in the high intensity region of western China, this paper adopts the onlinear time- history method to analyze the structural response of the overall bridge elastic-plastic model under seismic action. Under the force of Wolong wave in Wenchuan Earthquake, the main load-bearing members of the structure have a good seismic performance. Only part of the columns on the main arch ring enter the plastic hinge state for energy dissipation, and the lower columns have a more obvious energy dissipation effect than the higher ones. The result indicates that the design idea of structure which is similar to that of strong column and weak beam in ductility design, has the characteristics of "strong arch and weak beam"and can therefore improve the ability of structure to resist collapse under earthquake action. © Published under licence by IOP Publishing Ltd.

关键词： Energy dissipation

来源：评论

学校读者我要写书评

暂无评论

Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Moto: Enhancing Embedding with Multiple Joint Factors for Ch...

引用

International Conference on Pattern Recognition

作者： Xunzhu Tang Rujie Zhu Tiezhu Sun Shi Wang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Central Florida Orlando FL USA Momenta Suzhou China Institute of Computing Technology Chinese Academy beijing China

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, etc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F₁-score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.

关键词： Fuses Text categorization Semantics Pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Embedding dynamic attributed networks by modeling the evolution processes

arXiv

引用

arXiv 2020年

作者： Xu, Zenan Ou, Zijing Su, Qinliang Yu, Jianxing Quan, Xiaojun Lin, Zhenkun School of Data and Computer Science Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：