检索结果-内蒙古大学图书馆

作者： Xiang Ji Hu Liu Zizhao Wang Yuge Li Yongliang Tian Mingyu Huo School of Aeronautical Science &Engineering Beihang University State Key Laboratory of Virtual Reality Technology and System Aircraft Advanced Design Technology Key Laboratory of Ministry of Industry

Combat search and rescue（CSAR） is a significant part of joint combat operation. The isolated personnel in hostile territory is of great value to both sides. For the sake of extremely high cost and massive consumption of time in military exercises, virtual reality is a preferred way to conduct the experiment. Mixed reality, known as MR, allows users to interact with the virtual objects, as well as the real objects. With MR devices, commanders get a better awareness of both virtual combat and real environment. Agent-based modelling is a relatively new approach to modelling systems composed of interacting objects. By analyzing the decision-making process of agents, codes are written to drive the simulation. Finally, the whole system is applied to HoloLens to provide a holographic demonstration.

关键词： Combat search and rescue Mixed reality Agent-based modelling Kosovo war Finite-state machine

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Co-Saliency detection with Co-Attention fully convolutional network

arXiv

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arXiv 2020年

作者： Gao, Guangshuai Zhao, Wenting Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Xueyuan Road Haidian District Beijing100191 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China AI Lab of China Merchants Bank Shenzhen China

Co-saliency detection aims to detect common salient objects from a group of relevant images. Some attempts have been made with the Fully Convolutional Network (FCN) framework and achieve satisfactory detection results. However, due to stacking convolution layers and pooling operation, the boundary details tend to be lost. In addition, existing models often utilize the extracted features without discrimination, leading to redundancy in representation since actually not all features are helpful to the final prediction and some even bring distraction. In this paper, we propose a co-attention module embedded FCN framework, called as Co-Attention FCN (CA-FCN). Specifically, the co-attention module is plugged into the high-level convolution layers of FCN, which can assign larger attention weights on the common salient objects and smaller ones on the background and uncommon distractors to boost final detection performance. Extensive experiments on three popular co-saliency benchmark datasets demonstrate the superiority of the proposed CA-FCN, which outperforms state-of-the-arts in most cases. Besides, the effectiveness of our new co-attention module is also validated with ablation studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Estimating cumulus cloud shape from a single image 12th

Estimating cumulus cloud shape from a single image

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12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017

作者： Zhang, Yiming Zhang, Zili Hou, Jiayue Liang, Xiaohui State Key Laboratory of Virtual Reality System and Technology Beihang University Beijing China Shijiazhuang University Shijiazhuang China

ISBN: (纸本)9789811073885

Cumulus cloud is a typical kind of low-altitude cloud and bears detailed appearance. However, construct a cumulus cloud shape from image remains a challenging task. In previous works, cloud thickness is first calculated from intensities of the pixels and then cloud surface shape is obtained based on symmetry assumption. Given an image, an optimization problem is formulated based on the shape from shading(SFS) algorithm with unknown illumination. This paper addresses this problem and presents a simple and effective method which estimates the surface shape directly by leveraging the rich details of the cloud surface. In this paper, we propose two constraints: the boundary constraint and relative height constraint. Meanwhile, a multi-scale optimization technique is utilized and then back surface of the cloud is constructed. The experimental results show that our method can generate natural and realistic cumulus cloud shape with details like the image. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Clouds

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

arXiv

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arXiv 2023年

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany ETH Zürich Switzerland The Shenzhen Campus of Sun Yat-Sen University Sun Yat-Sen University China Bigo Technology Pte. Ltd Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023, CC BY-SA.

关键词： Image enhancement

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DHA: Supervised Deep Learning to Hash with an Adaptive Loss Function

DHA: Supervised Deep Learning to Hash with an Adaptive Loss ...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Jiehao Xu Chengyu Guo Qingjie Liu Jie Qin Yunhong Wang Li Liu State Key Laboratory of Virtual Reality Technology and System Beihang University China College of System Engineering National University of Defense Technology China Inception Institute of Artificial Intelligence Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Hashing, which refers to the binary embedding of high-dimensional data, has been an effective solution for fast nearest neighbor retrieval in large-scale databases due to its computational and storage efficiency. Recently, deep learning to hash has been attracting increasing attention since it has shown great potential in improving retrieval quality by leveraging the strengths of deep neural networks. In this paper, we consider the problem of supervised hashing and propose an effective model (i.e., DHA), which is able to generate compact and discriminative binary codes while preserving semantic similarities of original data with an adaptive loss function. The key idea is that we scale and shift the loss function to avoid the saturation of gradients during training, and simultaneously adjust the loss to adapt to different levels of similarities of data. We evaluate the proposed DHA on three widely-used benchmarks, i.e., NUS-WIDE, CIFAR-10, and MS COCO. The state-of-the-art image retrieval performance clearly shows the effectiveness of our method in learning discriminative hash codes for nearest neighbor retrieval.

关键词： Binary codes Hamming distance Training Machine learning Semantics Data models Quantization (signal)

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A split-and-recombine approach for follow-up query analysis

arXiv

引用

arXiv 2019年

作者： Liu, Qian Chen, Bei Liu, Haoyan Lou, Jian-Guang Fang, Lei Zhou, Bin Zhang, Dongmei State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China State Key Lab of Software Development Environment Beihang University China Microsoft Research Beijing China

Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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GA\SQP Optimization for Design of A Serial Robotic Arm Used As A Spatial Pointing Mechanism

GA\SQP Optimization for Design of A Serial Robotic Arm Used ...

引用

IEEE Conference on Cybernetics and Intelligent systems

作者： Xiaocong Wu Xiao Li Haowei Wang Guanyang Liu Chen Guan Chao Huang Ge Yu School of Mechanical Engineering & Automation Beihang University Beijing China Beijing Institute of Spacecraft System Engineering Beijing China School of Mechanical Engineering & Automation State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (数字)9781728134581

ISBN: (纸本)9781728134598

When designing a serial robotic arm based spatial pointing mechanism, a challenging problem is to identify all the design variables to satisfy all the requirements of launching, working conditions and adjustment of working state. Traditionally, engineers rely on experiences and use 3D CAD software to design it by trial and error, however, the approach is inefficient and impossible to provide the optimal solution. In this paper, we propose a genetic algorithm and sequential quadratic programming based methodology to solve the problem and develop a software for engineers to complete the dimensional design of a spatial pointing mechanism. Experimental results validate the effectiveness and efficiency of the proposed approach, and engineers use the developed software to design a spatial pointing robot conveniently and accurately.

关键词： Manipulators Optimization Robot kinematics Genetic algorithms Automation Mechanical engineering

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SHREC 2019 - Monocular image based 3D model retrieval 12

SHREC 2019 - Monocular image based 3D model retrieval

引用

12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for Computer Graphics, EG 2019

作者： Li, Wenhui Liu, Anan Nie, Weizhi Song, Dan Li, Yuqian Wang, Weijie Xiang, Shu Zhou, Heyu Bui, Ngoc-Minh Cen, Yunchi Chen, Zenian Chung-Nguyen, Huy-Hoang Diep, Gia-Han Do, Trong-Le Doubrovski, Eugeni L. Duong, Anh-Duc Geraedts, Jo M.P. Guo, Haobin Hoang, Trung-Hieu Li, Yichen Liu, Xing Liu, Zishun Luu, Duc-Tuan Ma, Yunsheng Nguyen, Vinh-Tiep Nie, Jie Ren, Tongwei Tran, Mai-Khiem Tran-Nguyen, Son-Thanh Tran, Minh-Triet Vu-Le, The-Anh Wang, Charlie C.L. Wang, Shijie Wu, Gangshan Yang, Caifei Yuan, Meng Zhai, Hao Zhang, Ao Zhang, Fan Zhao, Sicheng Shool of Electrical and Information Engineering Tianjin University China University of Science VNU-HCM Viet Nam State Key Laboratory of Virtual Reality Technology and System Beihang University China Delft University of Technology Netherlands University of Information Technology VNU-HCM Viet Nam Nanjing University China SuoAo Technology Center SAEE University of Science and Technology Beijing China Chinese University of Hong Kong Hong Kong School of Software Dalian University of Technology China Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Ocean University of China China

ISBN: (纸本)9783038680772

Monocular image based 3D object retrieval is a novel and challenging research topic in the field of 3D object retrieval. Given a RGB image captured in real world, it aims to search for relevant 3D objects from a dataset. To advance this promising research, we organize this SHREC track and build the first monocular image based 3D object retrieval benchmark by collecting 2D images from ImageNet and 3D objects from popular 3D datasets such as NTU, PSB, ModelNet40 and ShapeNet. The benchmark contains classified 21,000 2D images and 7,690 3D objects of 21 categories. This track attracted 9 groups from 4 countries and the submission of 20 runs. To have a comprehensive comparison, 7 commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. The evaluation results show that the supervised cross domain learning get the superior retrieval performance (Best NN is 97.4 %) by bridging the domain gap with label information. However, there is still a big challenge for unsupervised cross domain learning (Best NN is 61.2%), which is more practical for the real application. Although we provided both view images and OBJ file for each 3D model, all the participants use the view images to represent the 3D model. One of the interesting work in the future is directly using the 3D information and 2D RGB information to solve the task of monocular Image based 3D model retrieval. © 2019 The Author(s)

关键词： 3D modeling

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Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling 5th

Mobile APP User Attribute Prediction by Heterogeneous Inform...

引用

5th International Conference on Dependability in Sensor, Cloud, and Big Data systems and Applications, DependSys 2019

作者： Zhang, Hekai Gong, Jibing Teng, Zhiyong Wang, Dan Wang, Hongfei Du, Linfeng Bhuiyan, Zakirul Alam School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Lab for Computer Virtual Technology and System Integration Yanshan University Qinhuangdao066004 China School of Computer Science and Engineering Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214000 China Department of Computer and Information Sciences Fordham University New York United States

ISBN: (纸本)9789811513039

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. Although many scholars have made achievements in user attribute prediction and other related fields, there are still two main problems that impede further improvement on the accuracy of classification: (1) Traditional machine learning classification merely takes each object as a single individual, ignoring the relationship between them;(2) At present, the popular Heterogeneous Path-Mine Information Network only considers whether the user has a relationship with the attributes of other nodes, rather than the degree of correlation of the attributes. It employs a linear regression model to fit the weight of meta-path, which is highly sensitive to outliers. To solve the above two problems, this paper advances the HetPathMine model and puts forward TPathMine model. With applying the number of clicks of attributes under each node to express the user’s emotional preference information, optimizations of the solution of meta-path weight are also presented. Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects. Matrix is used to realize the knowledge dissemination of category knowledge among isomorphic objects. The experimental results show that: (1) the prediction of user attributes based on heterogeneous information networks can achieve higher accuracy than traditional machine learning classification methods;(2) TPathMine model based on the number of clicks is more accurate in classifying users of different age groups, and the weight of each meta-path is consistent with human intuition or the real world situation. © 2019, Spr

关键词： Forecasting

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Unmanned Aerial Vehicle Route Planning Method Based on A Star Algorithm

Unmanned Aerial Vehicle Route Planning Method Based on A Sta...

引用

IEEE Conference on Industrial Electronics and Applications

作者： Tianyou Chen Guofeng Zhang Xiaoguang Hu Jin Xiao The State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China

On the basis of studying and analyzing the basic principles of A star algorithm, this paper compares its advantages and disadvantages with those of genetic algorithm and ant colony algorithm. In the meantime, the paper builds the model of unmanned aerial vehicle (UAV) battlefield environment for route planning and applies the A star algorithm to UAV route planning under the constraints of bearing the least danger and consuming the least fuel. Finally, the paper uses python programming language to simulate the UAV route planning, whose result shows the validity of the model and the feasibility of the algorithm.

关键词： A~* algorithm Route planning Unmanned aerial vehicle

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