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Workshops held at the 16th European Conference on computer vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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An End-to-End Video Text Detector with Online Tracking

An End-to-End Video Text Detector with Online Tracking

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International Conference on Document Analysis and recognition

作者： Hongyuan Yu Chengquan Zhang Xuan Li Junyu Han Errui Ding Liang Wang University of Chinese Academy of Sciences (UCAS) Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Department of Computer Vision Technology(VIS) Baidu Inc. Chinese Academy of Sciences Artificial Intelligence Research (CAS-AIR)

Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., motion blur, illumination changes, and occlusion; 2) the properties of text including variants of fonts, languages, orientations, and shapes. Most existing methods attempt to enhance the performance of video text detection by cooperating with video text tracking, but treat these two tasks separately. In this work, we propose an end-to-end video text detection model with online tracking to address these two challenges. Specifically, in the detection branch, we adopt ConvLSTM to capture spatial structure information and motion memory. In the tracking branch, we convert the tracking problem to text instance association, and an appearance-geometry descriptor with memory mechanism is proposed to generate robust representation of text instances. By integrating these two branches into one trainable framework, they can promote each other and the computational cost is significantly reduced. Experiments on existing video text benchmarks including ICDAR2013 Video, Minetto and YVT demonstrate that the proposed method significantly outperforms state-of-the-art methods. Our method improves F-score by about 2% on all datasets and it can run realtime with 24.36 fps on TITAN Xp.

关键词： Feature extraction Geometry Task analysis Tracking Trajectory Proposals Loss measurement

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Risk-based decision making: estimands for sequential prediction under interventions

arXiv

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

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Artificial intelligent techniques and its applications

Artificial intelligent techniques and its applications

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作者： Sundhararajan, Mahalingam Gao, Xiao-Zhi Vahdat Nejad, Hamed Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Bharath University Chennai India Department of Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Finland Department of Computer Science and Engineering University of Birjand Birjand Iran

This special issue of the Journal of Intelligent & Fuzzy Systems is a selected collection of papers submitted to the IEEE International Conference on Algorithms, methodology, models and applications in emerging technologies and International Conference on Telecommunication, Power analysis and Computing Techniques and held from February 16-18, 2017 and April 6-8, 2017, Chennai, India. These papers have been reviewed and accepted for presentation at the conference and for publication in the Journal of Intelligent & Fuzzy Systems (JIFS). In this special issue there are 50 papers covering a wide range of tools, techniques and applications of artificial intelligent techniques and applications.

关键词： Fuzzy systems

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Geometry sharing network for 3D point cloud classification and segmentation

arXiv

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arXiv 2019年

作者： Xu, Mingye Zhou, Zhipeng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

In spite of the recent progresses on classifying 3D point cloud with deep CNNs, large geometric transformations like rotation and translation remain challenging problem and harm the final classification performance. To address this challenge, we propose Geometry Sharing Network (GS-Net) which effectively learns point descriptors with holistic context to enhance the robustness to geometric transformations. Compared with previous 3D point CNNs which perform convolution on nearby points, GS-Net can aggregate point features in a more global way. Specially, GS-Net consists of Geometry Similarity Connection (GSC) modules which exploit Eigen-Graph to group distant points with similar and relevant geometric information, and aggregate features from nearest neighbors in both Euclidean space and Eigenvalue space. This design allows GS-Net to efficiently capture both local and holistic geometric features such as symmetry, curvature, convexity and connectivity. Theoretically, we show the nearest neighbors of each point in Eigenvalue space are invariant to rotation and translation. We conduct extensive experiments on public datasets, ModelNet40, ShapeNet Part. Experiments demonstrate that GS-Net achieves the state-of-the-art performances on major datasets, 93.3% on ModelNet40, and are more robust to geometric transformations. Code is released on https://***/MingyeXu/GS-Net. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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Overview of the recent experimental research on the J-TEXT tokamak

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Nuclear Fusion 2024年第11期64卷 112005-112005页

作者： Ding, Yonghua Wang, Nengchao Chen, Zhongyong Xia, Donghui Yang, Zhoujun Chen, Zhipeng Zheng, Wei Yan, Wei Li, Da Zhou, Song Xu, Xin Zhang, Xiaoyi Mao, Feiyue Fang, Jiangang Ren, Zhengkang Chen, Xixuan Zhang, Junli Zhang, Xiaobo He, Ying Zhang, Qi Li, You Bai, Wei Mou, Lingke Li, Feng Long, Ting Ke, Rui Gao, Li Shi, Peng Shen, Chengshuo Liu, Jiaxing Guo, Weixin Wang, Lu Li, Hanhui Jiang, Zhonghe Zhang, Xiaoqing Li, Jianchao Rao, Bo Cheng, Zhifeng Zhu, Ping Liu, Minghai Xu, Tao Ma, Shaoxiang Yang, Yong Li, Chuan Wang, Zhijiang Zhang, Ming Yu, Kexun Hu, Xiwei Liang, Y. Yu, Q. Gentle, K.W. Pan, Yuan Gentle, Kenneth William Yu, Qingquan Liang, Yunfeng Jiang, Wei Yi, Lin Zhang, Ya Hu, Qiming Li, Jianchao Huang, Mingxiang Li, Haolong Huang, Zhuo Huang, Jie Wang, Weijun He, Mei Zhao, Qing Xu, Jiayu Zhou, Gangyi Ba, Weigang Yang, Cheng Yao, Junjie Wang, Chuliang He, Bo Hu, Yongzhen Deng, Mingyang Fu, Jingwei Liang, Meiling Guan, Yehong Wu, Hongyan Zhuang, Ge Ding, Weixing Lan, Tao Zeng, Shiyong Hussain, Malik Sadam Lin, Zhifang Liu, Hai Sun, Youwen Zeng, Long Liu, Shaocheng Liao, Liang Zhong, Wulyu Shi, Zhongbing Jiang, Min Dong, Yunbo Xu, Min Wang, Aike Zhang, Xiaolong Liu, Linzi Tong, Ruihai Cheng, Jun Nie, Lin Li, Hui Xu, Jianqiang Yang, Zengchen Wen, Jie Liang, Anshu Zhang, Yipo Wu, Yifan Chen, Chengyuan Zhao, Kaijun Shi, Yuejiang Huang, He Austin, Max Lee, Sanggon Luhmann, Neville C. Domier, Calvin W. Zhu, Yilun Brower, David L. Chen, Jie Ida, K. Takemura, Y. Suzuki, Y. Nagasaki, K. Drews, P. Knieps, A. Han, Xiang Krämer-Flecken, A. Henkel, M. He, Jiyang Zhou, Yinan Jia, Ruo Peng, Yue Wang, Pengyu Li, Jiawei Yang, Jie Lu, Conghui Lu, Yiwei Wang, Rumeng Zhang, Hongqi Zeng, Yingqin Zhang, Guodong Ma, Fangyuan Bala, Abba Alhaji Li, Dingchen Hua, Jiankun Ai, Xinkun Tao, Qinlin Zhong, Yu Li, Yangbo He, Xiangrui Wan, Sui Huang, Shanni Ma, Yiming Qu, Zitong Yu, Shimin Huang, Zhangsheng Lei, Chi School of Electric and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Southwestern Institute of Physics Chengdu China Institute of Plasma Physics Chinese Academy of Sciences Hefei China Guizhou University of Commerce Guiyang China Wuhan Institute of Technology Wuhan China ITER Organization Route de Vinon-sur-Verdon CS 90 046 Saint-Paul-lez-DuranceCedex 13067 France Forschungszentrum Jülich GmbH IEK-Plasmaphysik Jülich Germany Max-Planck-Institut für Plasmaphysik Garching Germany Institute of Fusion Studies University of Texas at Austin AustinTX United States University of Texas at Austin AustinTX78712 United States School of Physics Huazhong University of Science and Technology Wuhan430074 China Department of Physics Wuhan University of Technology Wuhan430070 China Princeton Plasma Physics Laboratory PO Box 451 PrincetonNJ08543 United States Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China College of Computer Science South-Central University for Nationalities Wuhan430074 China Shenzhen University Shenzhen518060 China University of Science and Technology of China Hefei230026 China School of Electrical Engineering & Automation Jiangsu Normal University Xuzhou221116 China Southwest Jiaotong University Chengdu China College of Nuclear Science and Engineer East China University of Technology PO Box Nanchang330013 China ENN Science and Technology Development Co. Ltd Hebei Key Laboratory of Compact Fusion Langfang065001 China National Fusion Research Institute Daejeon305-333 Korea Republic of University of California DavisCA95616 United States University of California Los Angeles Los AngelesCA90095 United States National Institute for Fusion Science Toki509-5292 Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8527 Japan Institute of Advanced Energy Kyoto University Gokasho Ky

The J-TEXT capability is enhanced compared to two years ago with several upgrades of its diagnostics and the increase of electron cyclotron resonance heating (ECRH) power to 1 MW. With the application of electron cyclotron wave (ECW), the ECW assisted plasma startup is achieved;the tearing mode is suppressed;the toroidal injection of 300 kW ECW drives around 24 kA current;fast electrons are generated with toroidal injected ECW and the runaway current conversion efficiency increases with ECRH power. The mode coupling between 2/1 and 3/1 modes are extensively studied. The coupled 2/1 and 3/1 modes usually lead to major disruption. Their coupling can be either suppressed or avoided by external resonant magnetic perturbation fields and hence avoids the major disruption. It is also found that the 2/1 threshold of external field is significantly reduced by a pre-excited 3/1 mode, which can be either a locked island or an external kink mode. The disruption control is studied by developing prediction methods capable of cross tokamak application and by new mitigation methods, such as the biased electrode or electromagnetic pellet injector. The high-density operation and related disruptions are studied from various aspects. Approaching the density limit, the collapse of the edge shear layer is observed and such collapse can be prevented by applying edge biasing, leading to an increased density limit. The density limit is also observed to increase, if the plasma is operated in the poloidal divertor configuration or the plasma purity is increased by increasing the pre-filled gas pressure or ECRH power during the start-up phase. © 2024 The Author(s).

关键词： Tokamak devices

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A CNN Based Framework for Unistroke Numeral recognition in Air-Writing

A CNN Based Framework for Unistroke Numeral Recognition in A...

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International Workshop on Frontiers in Handwriting recognition

作者： Prasun Roy Subhankar Ghosh Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata WB India

Air-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies greatly on the illumination condition due to color based segmentation. In a less fluctuating illumination condition the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework achieved 97.7%, 95.4% and 93.7% recognition rate in person independent evaluation over English, Bengali and Devanagari numerals, respectively.

关键词： Training Testing Sensors Cameras Writing Image color analysis Convolution

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Web Page Classification Algorithm Based on Semi-Supervised Support Vector Machine

Web Page Classification Algorithm Based on Semi-Supervised S...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Wenqing Huang Hui You School of Information The Institute of computer vision image processing and pattern recognition acceptable Hangzhou China

ISBN: (数字)9781538618035

ISBN: (纸本)9781538618042

Most web page classification algorithms are learning algorithms under the single-instance single-label framework. Multi-Instance Multi-Label learning is a new machine learning framework. MIMLSVM+ algorithm, using degenerate strategy to transform multiple instances into single instances, and then decomposing multiple labels into a series of two types of classification problems, that is, building an SVM for each label. Because the MIMLSVM+ algorithm degrades the multi-label problem into a series of two types of classification problems, the processing of each label will lose the contact information between the labels independently. Therefore, multi-tasking technology is introduced to make use of the multi-tasking learning framework based on nuclear to validate the MIMLSVM+ expand, get E-MIMLSVM+ algorithm. The paper improves E-MIMLSVM+ algorithm by using the semi-supervised learning method. Experimental results show that the proposed method can achieve higher classification accuracy.

关键词： Support vector machines Classification algorithms Semisupervised learning Web pages Training Supervised learning Machine learning algorithms

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CNN-RNN based method for license plate recognition

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CAAI Transactions on Intelligence Technology 2018年第3期3卷 169-175页

作者： Palaiahnakote Shivakumara Dongqi Tang Maryam Asadzadehkaljahi Tong Lu Umapada Pal Mohammad Hossein Anisi Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Laboratory for Novel Software Technology Nanjing Universiry Nanjing People's Republic of China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Computer Science and Electronic Engineering University of Essex CO4 3SQ Colchester UK

Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle （e.g., cars） have numbers with dark background, while public vehicle （taxis/cabs） have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks （CNN） and Recurrent Neural Networks namely, BLSTM （Bi-Directional Long Short Term Memory）, for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting （DCV）, which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.

关键词：车牌识别识别率发展现状人工智能

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Blind super-resolutionwith iterative kernel correction

arXiv

引用

arXiv 2019年

作者： Gu, Jinjin Lu, Hannan Zuo, Wangmeng Dong, Chao School of Science and Engineering Chinese University of Hong Kong Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Harbin China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

Deep learning based methods have dominated superresolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/ known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe performance drop for the advanced SR methods. In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown. We draw the observation that kernel mismatch could bring regular artifacts (either over-sharpening or over-smoothing), which can be applied to correct inaccurate blur kernels. Thus we introduce an iterative correction scheme - IKC that achieves better results than direct kernel estimation. We further propose an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD. Extensive experiments on synthetic and real-world images show that the proposed IKC method with SFTMD can provide visually favorable SR results and the state-of-the-art performance in blind SR problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

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