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Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency

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arXiv 2019年

作者： Li, Jia Fu, Kui Zhao, Shengwei Ge, Shiming State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security at University of Chinese Academy of Sciences Beijing100095 China

—The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, and the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be further removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach outperforms ten state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS on the GPU platform. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

VoxSegNet: Volumetric CNNs for semantic part segmentation of 3d shapes

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arXiv 2018年

作者： Wang, Zongji Lu, Feng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University International Research Institute for Multidisciplinary Science Beihang University Beijing100191 China

Voxel is an important format to represent geometric data, which has been widely used for 3D deep learning in shape analysis due to its generalization ability and regular data format. However, fine-grained tasks like part segmentation require detailed structural information, which increases voxel resolution and thus causes other issues such as the exhaustion of computational resources. In this paper, we propose a novel volumetric convolutional neural network, which could extract discriminative features encoding detailed information from voxelized 3D data under a limited resolution. To this purpose, a spatial dense extraction (SDE) module is designed to preserve the spatial resolution during the feature extraction procedure, alleviating the loss of detail caused by sub-sampling operations such as max-pooling. An attention feature aggregation (AFA) module is also introduced to adaptively select informative features from different abstraction scales, leading to segmentation with both semantic consistency and high accuracy of details. Experiment results on the large-scale dataset demonstrate the effectiveness of our method in 3D shape part segmentation. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantic Segmentation

AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

Data-Driven Hair Modeling from a Single Image

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Data-Driven Hair Modeling from a Single Image

International Conference on virtual reality and Visualization (ICVRV)

作者： Jiqiang Wu Yongtang Bao Yue Qi State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Beihang University Qingdao Research Institute Qingdao China

ISBN: (纸本)9781538684986;9781538684979

Hair is one of the most distinctive part of a human body, which is essential for the digitization of compelling virtual avatars. We present a data-driven approach for generating complete and complex 3D hairstyles from a single-view portrait. We construct a hairstyle database which contains more than 2000 hair models. Given a target hairstyle portrait image, we first draw a few strokes as guidance. We then search multiple best matching examples from the database based on our enhanced matching algorithm. Finally, we combine them consistently into a single hairstyle. The generated hairstyles are visually comparable to original portrait images. The reconstructed 3D hair models can be used for many applications, such as hair editing, and dynamic hair simulation.

关键词： Hair Solid modeling Three-dimensional displays Databases Computational modeling Two dimensional displays Head

Chinese guideline for the application of rectal cancer staging recognition systems based on artificial intelligence platforms (2021 edition)

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Chinese Medical Journal 2021年第11期134卷 1261-1263页

作者： Yuan Gao Yun Lu Shuai Li Yong Dai Bo Feng Fang-Hai Han Jia-Gang Han Jing-Jing He Xin-Xiang Li Guo-Le Lin Qian Liu Gui-Ying Wang Quan Wang Zhen-Ning Wang Zheng Wang Ai-Wen Wu Bin Wu Ying-Chi Yang Hong-Wei Yao Wei Zhang Jian-Ping Zhou Ai-Min Hao Zhong-Tao Zhang Colorectal Surgery Group of the Surgery Branch in the Chinese Medical Association Beihang University state key laboratory of virtual reality technology and systems Department of General Surgery The Affiliated Hospital of Qingdao UniversityQingdaoShandong 266555China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing 100191China Department of General Surgery Qilu Hospital of Shandong UniversityJinanShandong 250012China Department of General Surgery Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China Shanghai Minimally Invasive Surgery Center Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China Department of Gastrointestinal Surgery Sun Yat-sen UniversityGuangzhouGuangdong 510120China Department of General Surgery Beijing Chaoyang HospitalCapital Medical UniversityBeijing 100020China Institute of International Law Chinese Academy of Social SciencesBeijing 100732China Department of Colorectal Surgery Fudan University Shanghai Cancer CenterShanghai 201321China Department of Oncology Shanghai Medical CollegeFudan UniversityShanghai 201321China Department of General Surgery Peking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijing 100730China National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Third Hospital of Hebei Medical University ShijiazhuangHebei 050051China Department of Colon and Rectum Surgery The First Hospital of Jilin UniversityChangchunJilin 130021China Department of Surgical Oncology and General Surgery Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal TumorsMinistry of EducationThe First Affiliated Hospital of China Medical UniversityShenyangLiaoning 110001China Department of Gastrointestinal Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanHubei 430022China Department of Unit III&Ostomy Service Key laboratory of Carcinogenesis and Translational Research(Ministry of Education)Gastrointest

Development and Application of Artificial Intelligence Recognition systems in Rectal Cancer Staging Whether for surgical treatment or for neoadjuvant chemoradiotherapy,imaging evaluation has become an important basis to perform the treatment plans.[1]The reading of imaging results requires a large number of experienced radiologists to complete,but shortages and uneven distributions of personnel cause delays and biases in imaging ***,independent research and development of automatic recognition systems of rectal cancer staging based on artificial intelligence(AI)platforms aim to partially replace practitioners’work and achieve rapid and accurate identification of rectal cancer staging.

关键词： staging artificial replace

Computing restricted Voronoi diagram on graphics hardware 25

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Computing restricted Voronoi diagram on graphics hardware

25th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2017

作者： Han, Jiawei Yan, Dong-Ming Wang, Lili Zhao, Qinping State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9783038680512

The 3D restricted Voronoi diagram (RVD), defined as the intersection of the 3D Voronoi diagram of a pointset with a mesh surface, has many applications in geometry processing. There exist several CPU algorithms for computing RVDs. However, such algorithms still cannot compute RVDs in realtime. In this short paper, we propose an efficient algorithm for computing RVDs on graphics hardware. We demonstrate the robustness and the efficiency of the proposed GPU algorithm by applying it to surface remeshing based on centroidal Voronoi tessellation. © 2017 The Author(s) Eurographics Proceedings © 2017 The Eurographics Association.

关键词： Computational geometry

Image co-segmentation via multi-scale local shape transfer

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arXiv 2018年

作者： Teng, Wei Zhang, Yu Chen, Xiaowu Li, Jia He, Zhiqiang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China International Research Institute for Multidisciplinary Science Beihang University Beijing100191 China Lenovo Research China

Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category. In real-world cases, however, common foreground objects often vary greatly in appearance, making their global shapes highly inconsistent across images and difficult to be segmented. To address this problem, this paper proposes a novel co-segmentation approach that transfers patch-level local object shapes which appear more consistent across different images. In our framework, a multi-scale patch neighbourhood system is first generated using proposal flow on arbitrary image-pair, which is further refined by Locally Linear Embedding. Based on the patch relationships, we propose an efficient algorithm to jointly segment the objects in each image while transferring their local shapes across different images. Extensive experiments demonstrate that the proposed method can robustly and effectively segment common objects from an image set. On iCoseg, MSRC and Coseg-Rep dataset, the proposed approach performs comparable or better than the state-of-the-arts, while on a more challenging benchmark Fashionista dataset, our method achieves significant improvements. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

Research on multiple-split load sharing characteristics of 2-stage external meshing star gearing system

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Research on multiple-split load sharing characteristics of 2...

International Conference on Gear Production, 2017 and International Conference on High Performance Plastic Gears, 2017

作者： Shuai, Mo Shuai, Ma Guoguang, Jin Yidu, Zhang Chao, L.V. Haruo, Houjoh School of Mechanical Engineering Tianjin Polytechnic University Tianjin China The State Key Laboratory of Mechanical Transmissions Chongqing University Chongqin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Tianjin Tanhas Group Co Ltd Tianjin China Precision and Intelligence Laboratory Tokyo Institute of Technology Yokohama Japan The State Key Laboratory of Mechanical Transmissions Chongqing University Chongqin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Tianjin Tanhas Group Co Ltd Tianjin China Precision and Intelligence Laboratory Tokyo Institute of Technology Yokohama Japan

ISBN: (纸本)9783180923192

Conducting a further analysis on multiple-split loading sharing among star gears of 2-stage external gearing star gearing system in Aero-engine, and a meshing error analysis on the eccentricity error, gear thickness error, base pitch error, assembly error, and bearing manufacturing error of gear components of the star gear train respectively, and considering the floating meshing error resulting from meshing clearance variation caused by the simultaneous floating of all gears in the system, this research establishes a refined mathematical model of 2-stage external gearing 3-split loading sharing coefficient calculation in consideration of displacement compatibility, obtains the regular curves of the load sharing coefficient and floating orbit, which provides scientific theory evidence for proper tolerance distribution and control in design and process. © VDI Verlag GmbH Düsseldorf 2017.

关键词： Gears

Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks

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Very High Resolution Images Classification by Fusing Deep Co...

2017 5th International Conference on Advanced Computer Science Applications and Technologies(ACSAT 2017)

作者： Meziane Iftene Qingjie Liu Yunhong Wang State Key Laboratory of Virtual Reality Technology and Systems Beihang University

Recently, deep convolutional neural networks(CNNs) have made great achievements, whether taken as features extractor or classifier, in particular for very high resolution(VHR) images classification task which is a key point in the remote sensing field. This work aims to improve the VHR image classification accuracy by exploiting the fusion of two pre-trained deep convolutional neural network models. In this paper, we propose to concatenate the features extracted from the last convolutional layer of each pre-trained deep convolutional neural network to get a long features vector which is fed into a fullyconnected layer and then perform a fine-tuning for a VHR image dataset classification. The experimental results are promising since they show that the fusion of two deep CNNs achieves better accuracy for the classification compared to the individual CNN models on the same dataset.

关键词： CNN Fusion Convolutional neural networks Fine-tuning VHR images Classification