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Advancing image understanding in poor visibility environments: A collective benchmark study

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye Ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, Di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, Ruizhe Yi, Dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Author Correction:data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 476-476页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China School of Materials Science and Engineering University of Science and Technology Beijing Beijing China School of Materials Science and Technology Liaoning Technical University Liaoning China The Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa Tokyo Japan National Intellectual Property Administration Beijing China Faculty of Engineering Sciences University of Burundi Bujumbura Burundi Faculty of Engineering and Technology Palestine Technical University – Kadoorie Tulkarem Palestine College of Information Science and Engineering China University of Petroleum Beijing China Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent science and Engineering,University of science and Tec... 详细信息

关键词： HTML knowledge mining

MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation

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arXiv 2018年

作者： Zhang, Jiawei Zhang, Yanchun Jin, Yuzhen Xu, Jilan Xu, Xiaowei The Department of New Networks Peng Cheng Laboratory Guangdong Shenzhen China Southern Medical University Guangdong Guangzhou China Cyberspace Institute of Advanced Technology Guangzhou University Guangdong Guangzhou China Institute for Sustainable Industries & Livable Cities Victoria University MelbourneVIC Australia Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

Biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions to biomedical image segmentation applications. In this paper, we propose three different multi-scale dense connections (MDC) for the encoder, the decoder of U-shaped architectures, and across them. Based on three dense connections, we propose a multi-scale densely connected U-Net (MDU-Net) for biomedical image segmentation. MDU-Net directly fuses the neighboring feature maps with different scales from both higher layers and lower layers to strengthen feature propagation in the current layer. Multi-scale dense connections, which contain shorter connections between layers close to the input and output, also make a much deeper U-Net possible. Besides, we introduce quantization to alleviate the potential overfitting in dense connections, and further improve the segmentation performance. We evaluate our proposed model on the MICCAI 2015 Gland Segmentation (GlaS) dataset. The three MDC improve U-Net performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile, the MDU-Net with quantization obviously improves the segmentation performance of original U-Net. Copyright © 2018, The Authors. All rights reserved.

关键词： Image segmentation

Self-Propelled Magnetic Dendrite-Shaped Microrobots for Photodynamic Prostate Cancer Therapy

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Angewandte Chemie 2022年第48期134卷

作者： Xia Peng Mario Urso Jan Balvan Michal Masarik Martin Pumera Future Energy and Innovation Laboratory Central European Institute of Technology Brno University of Technology Purkynova 123 61200 Brno Czech Republic Contribution: Data curation (lead) Department of Physiology Faculty of Medicine Masaryk University Kamenice 5 62500 Brno Czech Republic Department of Pathological Physiology Faculty of Medicine Masaryk University Kamenice 5 62500 Brno Czech Republic BIOCEV First Faculty of Medicine Charles University Prumyslova 595 25250 Vestec Czech Republic Department of Chemistry and Biochemistry Mendel University Zemedelska 1 61300 Brno Czech Republic Department of Medical Research China Medical University Hospital China Medical University No. 91 Hsueh-Shih Road 40402 Taichung Taiwan Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 17. listopadu 2172/15 70800 Ostrava Czech Republic Department of Chemical and Biomolecular Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul 03722 Korea

Photocatalytic micromotors that exhibit wireless and controllable motion by light have been extensively explored for cancer treatment by photodynamic therapy (PDT). However, overexpressed glutathione (GSH) in the tumor microenvironment can down-regulate the reactive oxygen species (ROS) level for cancer therapy. Herein, we present dendrite-shaped light-powered hematite microrobots as an effective GSH depletion agent for PDT of prostate cancer cells. These hematite microrobots can display negative phototactic motion under light irradiation and flexible actuation in a defined path controlled by an external magnetic field. Non-contact transportation of micro-sized cells can be achieved by manipulating the microrobot's motion. In addition, the biocompatible microrobots induce GSH depletion and greatly enhance PDT performance. The proposed dendrite-shaped hematite microrobots contribute to developing dual light/magnetic field-powered micromachines for the biomedical field.

关键词： Cargo Transportation Micromachines Micromotors Photocatalysis Tumor Cells

Microwave frequency upconversion employing a coupling-modulated ring resonator

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Photonics Research 2017年第6期5卷 208-213页

作者： YIMING ZHONG LINJIE ZHOU YANYANG ZHOU YUJIE XIA SIQI LIU LIANGJUN LU JIANPING CHEN XINGJUN WANG Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking University

We present a method to generate a frequency-doubled microwave signal by employing a coupling-modulated ring resonator. Critical coupling is achieved when the resonator intrinsic loss is perfectly balanced by the external coupling enabled by a Mach–Zehnder interferometer coupler. The high suppression of the carrier leads to a clean two-tone optical signal with the frequency interval two times larger than that of the input microwave *** beating of the two-tone signal at a photodiode generates the frequency upconverted microwave signal.A theoretical model is established to analyze the modulation process and the microwave signal *** results show that the electrical harmonic suppression ratio is around ~20 dB(29 dB) for an input microwave signal with 5 dBm(10 dBm) power.

关键词： Microwave frequency upconversion employing a coupling-modulated ring resonator MZI

ICMLCA 2021 - 2nd International Conference on Machine Learni...

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Preface

ICMLCA 2021 - 2nd International Conference on Machine Learning and computer Application 2021年 iii页

作者： Ning, Xiansheng Feng, Yongxin Zhang, Wenbo Yue, Mingkai Du, Ke-Lin Huang, Shutao Zhou, Fang Ba, Shuhong Fu, Chong Shui, Penglang Xu, Shuwen Wu, Sheng Wang, Shushan Du, Zhonghua Gao, Zhijun Fan, Chunlong Li, Yufeng Wang, Yupeng Liu, Qingli Yu, Jingang Yin, Zhenyu Zhang, Deyu Gao, Hongwei Wen, Feng Guo, Cean Hao, Yongping Chan, Yung-Kuan Pavlovich, Vinogradov Gennady Kirpichnikova, Irina Qian, Zhihong Ivanov, Dmitriy V. Zhou, Xueguan Nezamabadi-Pour, Hossein Yang, Qing Zhang, Yongtang Liao, Haibin Huang, Ying Srinivasan, Kathiravan Xu, Zhenghua Pachori, Ram Bilas Zhang, Jialu AlShawabkeh, Mahmoud Guo, Chengjun Chaugule, Archana Ajit Lima, Alberto Sampaio Liu, Huayong Vidal, Jorge Maestre Alrshah, Mohamed A. Ghafoor, Kayhan Zrar Kubicek, Jan Chaugule, Archana Ajit Rodríguez-Pérez, Miguel Kumar, Gulshan Nasri, Mehdi Borrego, Carlos Persico, Valerio Wagh, Sanjeev Yan, Wenjun Yang, Li Yang, Dawei Xing, Yongkang Yang, Huadong Shang, Chengguo Chang, Xingong Shi, Hongbo Li, Aijun Feng, Liping Lv, Yali Liang, Minfu Wang, Kaixuan Bai, Zengliang Liang, Anhui Nan, Zhihong Ji, Suqin Chang, Liwei Zhou, Hongbin Yao, Chunli Cao, Guodong Zhang, Juling School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Concordia University Canada School of Automotive and Transportation Shenyang Ligong University China School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Northeastern University China Xidian University China Beijing University of Posts and Telecommunications China Beijing Institute of Technology China Nanjing University of Science & Technology China Shenyang Jianzhu University China Shenyang Aerospace University China Dalian University China Shenyang Institute of Computing Technology Chinese Academy of Sciences China Shenyang Ligong University China National Chun Hsing University Taiwan Department of Informatics and Applied Mathematics Russia South Ural State University Russia Jilin University China Samara State University of Transport Russia Naval Universit of Engineering China Shahid Bahonar University of Kermanto Iran Central China Normal University China Guangdong Neusoft Institute China Hubei University of Science and Technology China Liuzhou Railway Vocational Technical College China Vellore Institute of Technology India Hebei University of Technology China Indian Institute of Technology Indore India Xiangnan University China Guangxi Normal University for Nationalities China UESTC China Pimpri-Chinchwad College of Engineering & Research India Federal University of Ceará Brazil Complutense University of Madrid Spain Universiti Putra Malaysia Malaysia Shanghai Jiao Tong University China VŠB - Technical University of Ostrava Cuw Pimpri-Chinchwad College of Engineering & Research Ravet Pune India University of Vigo Spain Division of Research and Development India Islamic Azad University Iran Departament Enginyeria of Information Comunicacions Autonomous University of Barcelona Spain Opportunistic Networks United States Univers

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

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arXiv 2018年

作者： Alvermann, Andreas Basermann, Achim Bungartz, Hans-Joachim Carbogno, Christian Ernst, Dominik Fehske, Holger Futamura, Yasunori Galgon, Martin Hager, Georg Huber, Sarah Huckle, Thomas Ida, Akihiro Imakura, Akira Kawai, Masatoshi Köcher, Simone Kreutzer, Moritz Kus, Pavel Lang, Bruno Lederer, Hermann Manin, Valeriy Marek, Andreas Nakajima, Kengo Nemec, Lydia Reuter, Karsten Rippl, Michael Röhrig-Zöllner, Melven Sakurai, Tetsuya Scheffler, Matthias Scheurer, Christoph Shahzad, Faisal Brambila, Danilo Simoes Thies, Jonas Wellein, Gerhard University of Greifswald Institute of Physics Köln Germany Technical University of Munich Department of Informatics Fritz Haber Institute of the Max Planck Society Berlin Germany University of Erlangen-Nuremberg High Performance Computing University of Tsukuba Applied Mathematics University of Wuppertal Mathematics and Natural Sciences University of Tokyo Computer Science Technical University of Munich Department of Theoretical Chemistry Max Planck Computing and Data Facility Garching Germany

We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained. Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

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NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

PCL: Proposal cluster learning for weakly supervised object detection

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arXiv 2018年

作者： Tang, Peng Wang, Xinggang Bai, Song Shen, Wei Bai, Xiang Liu, Wenyu Yuille, Alan School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China Departments of Cognitive Science and Computer Science Johns Hopkins University BaltimoreMD21218-2608 United States

Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a novel deep network for WSOD. Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning (MIL), our strategy generates proposal clusters to learn refined instance classifiers by an iterative process. The proposals in the same cluster are spatially adjacent and associated with the same object. This prevents the network from concentrating too much on parts of objects instead of whole objects. We first show that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then show that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method. The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one. Experiments are conducted on the PASCAL VOC, ImageNet detection, and MS-COCO benchmarks for WSOD. Results show that our method outperforms the previous state of the art significantly. Copyright © 2018, The Authors. All rights reserved.

关键词： Object detection