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arXiv 2024年

作者： Zhou, Kanglei Cai, Ruizhi Wang, Liyuan Shum, Hubert P.H. Liang, Xiaohui State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China The Department of Computer Science and Technology Institute for AI BNRist Center THBI Lab Tsinghua-Bosch Joint Center for ML Tsinghua University Beijing100190 China The Department of Computer Science Durham University DurhamDH1 3LE United Kingdom The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Zhongguancun Laboratory Beijing China

Action Quality Assessment (AQA) quantitatively evaluates the quality of human actions, providing automated assessments that reduce biases in human judgment. Its applications span domains such as sports analysis, skill assessment, and medical care. Recent advances in AQA have introduced innovative methodologies, but similar methods often intertwine across different domains, highlighting the fragmented nature that hinders systematic reviews. In addition, the lack of a unified benchmark and limited computational comparisons hinder consistent evaluation and fair assessment of AQA approaches. In this work, we address these gaps by systematically analyzing over 150 AQA-related papers to develop a hierarchical taxonomy, construct a unified benchmark, and provide an in-depth analysis of current trends, challenges, and future directions. Our hierarchical taxonomy categorizes AQA methods based on input modalities (video, skeleton, multi-modal) and their specific characteristics, highlighting the evolution and interrelations across various approaches. To promote standardization, we present a unified benchmark, integrating diverse datasets to evaluate the assessment precision and computational efficiency. Finally, we review emerging task-specific applications and identify under-explored challenges in AQA, providing actionable insights into future research directions. This survey aims to deepen understanding of AQA progress, facilitate method comparison, and guide future innovations. The project web page can be found at https://***/AQA-Survey. Copyright © 2024, The Authors. All rights reserved.

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An Artificial Neural Network Based Haptic Rendering of Contact with Deformable Bodies

An Artificial Neural Network Based Haptic Rendering of Conta...

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IEEE International Conference on Mechatronics and Automation

作者： Wei Chen Guanyang Liu Yuru Zhang Bijan Shirinzadeh State Key Lab of Virtual Reality Technology and Systems Beihang University No.37 Xueyuan Road Haidian District Beijing 100191 China Department of Mechanical and Aerospace Engineering University of Monash Clayton VIC-3800 Australia

ISBN: (纸本)9781509023974

This paper presents an artificial neural network based 3-DOF haptic rendering scheme to render the contact force between a rigid object and a deformable body in a virtual environment. The finite-element method (FEM) technique is widely used for solving the deformation problem. However, this method has a heavy computational load to get accurate result, so it is difficult to apply this method to haptic simulating. To solve the challenging problem, in this paper, we presented a new motion control scheme to divide the motion of a rigid virtual object into three sub-movements along the three axes of a Cartesian-coordinate, based on which three single-input and single-output neural networks can be separately used to compute the three feedback force components along all the coordinate axes. The vector composition of the three force components is the feedback force exerted to a user through a haptic device. The proposed method can ensure the high accuracy and the high update rate of 3-DOF haptic rendering of deformable bodies. To testify the accuracy of the artificial neural network for haptic rendering, a medical robot is used to measure the data of the neural network training in the physical world, and a haptic device based experiment with a virtual environment validates the proposed algorithm for 3-DOF haptic rendering.

关键词： Artificial neural network 3-DOF haptic rendering The FEM method

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3D reconstruction from endoscopy images: A survey

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Computers in Biology and Medicine 2024年 175卷 108546-108546页

作者： Yang, Zhuoyue Dai, Ju Pan, Junjun State Key Laboratory of Virtual Reality Technology and Systems Beihang University 37 Xueyuan Road Haidian District Beijing100191 China Peng Cheng Lab 2 Xingke 1st Street Nanshan District ShenzhenGuangdong Province518000 China

Three-dimensional reconstruction of images acquired through endoscopes is playing a vital role in an increasing number of medical applications. Endoscopes used in the clinic are commonly classified as monocular endoscopes and binocular endoscopes. We have reviewed the classification of methods for depth estimation according to the type of endoscope. Basically, depth estimation relies on feature matching of images and multi-view geometry theory. However, these traditional techniques have many problems in the endoscopic environment. With the increasing development of deep learning techniques, there is a growing number of works based on learning methods to address challenges such as inconsistent illumination and texture sparsity. We have reviewed over 170 papers published in the 10 years from 2013 to 2023. The commonly used public datasets and performance metrics are summarized. We also give a taxonomy of methods and analyze the advantages and drawbacks of algorithms. Summary tables and result atlas are listed to facilitate the comparison of qualitative and quantitative performance of different methods in each category. In addition, we summarize commonly used scene representation methods in endoscopy and speculate on the prospects of deep estimation research in medical applications. We also compare the robustness performance, processing time, and scene representation of the methods to facilitate doctors and researchers in selecting appropriate methods based on surgical applications. © 2024 Elsevier Ltd

关键词： Endoscopy

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JobGrid: Chinese College Graduates Employment Information Grid System

JobGrid: Chinese College Graduates Employment Information Gr...

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ChinaGrid Annual Conference (ChinaGrid)

作者： Feng Zhao Bin Zhou Xiaoxing Sun Hai Jin Song Wu School of Computer Science and Technology Huazhong University of Science & Technology Wuhan China Cluster and Grid Computing Lab Wuhan China The State Key Laboratory of Virtual Reality Technology and Systems Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9780769533063

This paper proposes a realistic data grid system named JobGrid to construct information platform for Chinese college graduates employment, which supports centralized transaction processing to ensure consistent, effective operation in data access, control, monitor and analysis, and distributed data management to provide heterogeneous data integration, conversion, exaction and export. JobGrid has implemented global uniform view and massively heterogeneous data access, and provided efficiently data and transactions processing and cooperative control mechanism for distributed resource over grid.

关键词： Databases Educational institutions Distributed databases Servers Monitoring Computer architecture Employment

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Intrinsic relationship reasoning for small object detection

arXiv

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arXiv 2020年

作者： Fu, Kui Li, Jia Ma, Lin Mu, Kai Tian, Yonghong State Key Lab. of Virtual Reality Technology and Systems Beihang University China Tencent AI Laboratory Shenzhen China School of Electronics Engineering and Computer Science Peking University China Peng Cheng Laboratory Shenzhen China

The small objects in images and videos are usually not independent individuals. Instead, they more or less present some semantic and spatial layout relationships with each other. Modeling and inferring such intrinsic relationships can thereby be beneficial for small object detection. In this paper, we propose a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects. Specifically, we first construct a semantic module to model the sparse semantic relationships based on the initial regional features, and a spatial layout module to model the sparse spatial layout relationships based on their position and shape information, respectively. Both of them are then fed into a context reasoning module for integrating the contextual information with respect to the objects and their relationships, which is further fused with the original regional visual features for classification and regression. Experimental results reveal that the proposed approach can effectively boost the small object detection performance. Copyright © 2020, 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 ...

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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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Visualizing timelines: Evolutionary summarization via iterative reinforcement between text and image streams 12

Visualizing timelines: Evolutionary summarization via iterat...

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21st ACM International Conference on Information and Knowledge Management, CIKM 2012

作者： Yan, Rui Wan, Xiaojun Lapata, Mirella Zhao, Wayne Xin Cheng, Pu-Jen Li, Xiaoming Dept. of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Institute of Computer Science and Technology Peking University Beijing 100871 China Institute for Language Cognition and Computation University of Edinburgh Edinburgh EH8 9AB United Kingdom Dept. of Computer Science and Technology Peking University Beijing 10087 China State Key Lab. of Virtual Reality Tech. and Systems Beihang University Beijing 100083 China

ISBN: (纸本)9781450311564

We present a novel graph-based framework for timeline summarization, the task of creating different summaries for different timestamps but for the same topic. Our work extends timeline summarization to a multimodal setting and creates timelines that are both textual and visual. Our approach exploits the fact that news documents are often accompanied by pictures and the two share some common content. Our model optimizes local summary creation and global timeline generation jointly following an iterative approach based on mutual reinforcement and co-ranking. In our algorithm, individual summaries are generated by taking into account the mutual dependencies between sentences and images, and are iteratively refined by considering how they contribute to the global timeline and its coherence. Experiments on real-world datasets show that the timelines produced by our model outperform several competitive baselines both in terms of ROUGE and when assessed by human evaluators. © 2012 ACM.

关键词： Reinforcement

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Deco: False data detection and correction framework for participatory sensing

Deco: False data detection and correction framework for part...

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International Workshop on Quality of Service

作者： Long Cheng Linghe Kong Chengwen Luo Jianwei Niu Yu Gu Wenbo He Sajal Das State Key Lab of Networking & Switching Tech. Beijing Univ. of Posts & Telecomm. Beijing China State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China McGill University Canada National University of Singapore Singapore IBM Research Austin USA Missouri University of Science and Technology USA

Participatory sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of participatory sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth the important issues of false data detection and correction in participatory sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose DECO, a general framework to detect false values for participatory sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, DECO is able to accurately detect the false data and estimate both false and missing values for data correction. We validate our design through an experimental case study.

关键词： Sensors Quality of service IEEE 802.11 Standard Atmospheric measurements Particle measurements Compressed sensing Estimation

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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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Multi-task rank learning for image quality assessment 40

Multi-task rank learning for image quality assessment

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Xu, Long Li, Jia Lin, Weisi Zhang, Yongbing Ma, Lin Fang, Yuming Zhang, Yun Yan, Yihua Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering BeihangUniversity Beijing China International Research Institute for Multidisciplinary Science Beihang University Beijing China Department of Computer Engineering Nanyang Technological University Singapore Graduate School at Shenzhen Tsinghua University Shenzhen China Huawei Noah's Ark Lab Hong Kong School of Information Technology Jiangxi University of Finance and Economics Nanchang China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781467369978

In practice, multiple types of distortions are associated with an image quality degradation process. The existing machine learning (ML) based image quality assessment (IQA) approaches generally established a unified model for all distortion types, or each model is trained independently for each distortion type by using single-task learning, which lead to the poor generalization ability of the models as applied to practical image processing. There are often the underlying cross relatedness amongst these single-task learnings in IQA, which is ignored by the previous approaches. To solve this problem, we propose a multi-task learning framework to train IQA models simultaneously across individual tasks each of which concerns one distortion type. These relatedness can be therefore exploited to improve the generalization ability of IQA models from single-task learning. In addition, pairwise image quality rank instead of image quality rating is optimized in learning task. By mapping image quality rank to image quality rating, a novel no-reference (NR) IQA approach can be derived. The experimental results confirm that the proposed Multi-task Rank Learning based IQA (MRLIQ) approach is prominent among all state-of-the-art NR-IQA approaches. © 2015 IEEE.

关键词： image quality assessment machine learning MOS pairwise comparison Rank learning

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