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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Deep Multi-Model Fusion for Single-Image Dehazing

Deep Multi-Model Fusion for Single-Image Dehazing

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International Conference on computer vision (ICCV)

作者： Zijun Deng Lei Zhu Xiaowei Hu Chi-Wing Fu Xuemiao Xu Qing Zhang Jing Qin Pheng-Ann Heng South China University of Technology Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology CAS The Chinese University of Hong Kong State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Sun Yat-sen University The Hong Kong Polytechnic University CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology CAS

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional feature integration module to maximize the integration of the convolutional neural network (CNN) features at different CNN layers and generate the attentional multi-level integrated features (AMLIF). Then, from the AMLIF, we further predict a haze-free result for an atmospheric scattering model, as well as for four haze-layer separation models, and then fuse the results together to produce the final haze-free image. To evaluate the effectiveness of our method, we compare our network with several state-of-the-art methods on two widely-used dehazing benchmark datasets, as well as on two sets of real-world hazy images. Experimental results demonstrate clear quantitative and qualitative improvements of our method over the state-of-the-arts.

关键词： Atmospheric modeling Predictive models Scattering Computational modeling Neural networks computer vision Solid modeling

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SoccerNet 2023 Challenges Results

arXiv

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arXiv 2023年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Magera, Floriane Zhou, Xin Mkhallati, Hassan Deliège, Adrien Held, Jan Hinojosa, Carlos Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Kamal, Abdullah Maglo, Adrien Clapés, Albert Abdelaziz, Amr Xarles, Artur Orcesi, Astrid Scott, Atom Liu, Bin Lim, Byoungkwon Chen, Chen Deuser, Fabian Yan, Feng Yu, Fufu Shitrit, Gal Wang, Guanshuo Choi, Gyusik Kim, Hankyul Guo, Hao Fahrudin, Hasby Koguchi, Hidenari Ardö, Håkan Salah, Ibrahim Yerushalmy, Ido Muhammad, Iftikar Uchida, Ikuma Be'ery, Ishay Rabarisoa, Jaonary Lee, Jeongae Fu, Jiajun Yin, Jianqin Xu, Jinghang Nang, Jongho Denize, Julien Li, Junjie Zhang, Junpei Kim, Juntae Synowiec, Kamil Kobayashi, Kenji Zhang, Kexin Habel, Konrad Nakajima, Kota Jiao, Licheng Ma, Lin Wang, Lizhi Wang, Luping Li, Menglong Zhou, Mengying Nasr, Mohamed Abdelwahed, Mohamed Liashuha, Mykola Falaleev, Nikolay Oswald, Norbert Jia, Qiong Pham, Quoc-Cuong Song, Ran Hérault, Romain Peng, Rui Chen, Ruilong Liu, Ruixuan Baikulov, Ruslan Fukushima, Ryuto Escalera, Sergio Lee, Seungcheon Chen, Shimin Ding, Shouhong Someya, Taiga Moeslund, Thomas B. Li, Tianjiao Shen, Wei Zhang, Wei Li, Wei Dai, Wei Luo, Weixin Zhao, Wending Zhang, Wenjie Yang, Xinquan Ma, Yanbiao Joo, Yeeun Zeng, Yingsen Gan, Yiyang Zhu, Yongqiang Zhong, Yujie Ruan, Zheng Li, Zhiheng Huang, Zhijian Meng, Ziyu Belgium Saudi Arabia Sportradar Norway UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States Belgium Sharif University of Technology Iran Footovision France Zewail City of Science Technology and Innovation Egypt Université Paris-Saclay CEA France Universitat de Barcelona Spain Computer Vision Center Spain Nagoya University Japan Research Center for Applied Mathematics and Machine Intelligence Zhejiang Lab China AIBrain United States OPPO Research Institute China Germany Meituan China Tencent Youtu Lab China Amazon Prime Video Sport United States Sogang University Korea Republic of The University of Tokyo Japan Spiideo Sweden University of Tsukuba Japan School of Artificial Intelligence Beijing University of Posts and Telecommunications China Normandie Univ INSA Rouen LITIS France Shanghai Jiao Tong University China Key Laboratory of Intelligent Perception and Image Understanding The Ministry of Education Xidian University China NASK - National Research Institute Poland Robo Space China Tongji University China Sportlight Technology United Kingdom School of Control Science and Engineering Shandong University China lRomul Russia Aalborg University Denmark Turing AI Cultures GmbH Germany Information Systems Technology and Design Singapore University of Technology and Design Singapore Sun Yat-sen University China

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leader-boards are available on https://***. Baselines and development kits can be found on https://***/SoccerNet. © 2023, CC BY.

关键词： computer vision

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Learning from large-scale noisy web data with ubiquitous reweighting for image classification

arXiv

引用

arXiv 2018年

作者： Li, Jia Song, Yafei Zhu, Jianfeng Cheng, Lele Su, Ying Ye, Lin Yuan, Pengcheng Han, Shumin State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Shenzhen Cyberspace Laboratory Shenzhen China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing100871 China Computer Vision Technology Department of Baidu Beijing100871 China

Many advances of deep learning techniques originate from the efforts of addressing the image classification task on large-scale datasets. However, the construction of such clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that learns an image classification model from large-scale noisy data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. To alleviate these challenges, we assume that every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags and the labels. In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the Webvision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks the first place in the image classification task. [***] 3 Apr 2019. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

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Learning a saliency evaluation metric using crowdsourced perceptual judgments

arXiv

引用

arXiv 2018年

作者： Xia, Changqun Li, Jia Su, Jinming Borji, Ali State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China International Research Institute for Multidisciplinary Science Beihang University Beijing China Center for Research in Computer Vision Computer Science Department University of Central Florida OrlandoFL United States

In the area of human fixation prediction, dozens of computational saliency models are proposed to reveal certain saliency characteristics under different assumptions and definitions. As a result, saliency model benchmarking often requires several evaluation metrics to simultaneously assess saliency models from multiple perspectives. However, most computational metrics are not designed to directly measure the perceptual similarity of saliency maps so that the evaluation results may be sometimes inconsistent with the subjective impression. To address this problem, this paper first conducts extensive subjective tests to find out how the visual similarities between saliency maps are perceived by humans. Based on the crowdsourced data collected in these tests, we conclude several key factors in assessing saliency maps and quantize the performance of existing metrics. Inspired by these factors, we propose to learn a saliency evaluation metric based on a two-stream convolutional neural network using crowdsourced perceptual judgements. Specifically, the relative saliency score of each pair from the crowdsourced data is utilized to regularize the network during the training process. By capturing the key factors shared by various subjects in comparing saliency maps, the learned metric better aligns with human perception of saliency maps, making it a good complement to the existing metrics. Experimental results validate that the learned metric can be generalized to the comparisons of saliency maps from new images, new datasets, new models and synthetic data. Due to the effectiveness of the learned metric, it also can be used to facilitate the development of new models for fixation prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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Corrections to “Label-Free Detection of DNA Hybridization Utilizing Dual S-Tapered Thin-Core Fiber Interferometer”

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Journal of Lightwave Technology 2019年第19期37卷 5158-5158页

作者： Xu Zhang Bo Liu Hao Zhang Binbin Song Jixuan Wu Shaoxiang Duan Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology College of Electronic Information and Optical Engineering Nankai University Tianjin China Key Laboratory of Computer Vision and Systems Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin China College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin China

Presents corrections to above named paper.

关键词： Biosensors DNA Optical fiber sensors Molecular biophysics Microsensors

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NTIRE 2020 challenge on perceptual extreme super-resolution: Methods and results

arXiv

引用

arXiv 2020年

作者： Zhang, Kai Gu, Shuhang Timofte, Radu Shang, Taizhang Dai, Qiuju Zhu, Shengchen Yang, Tong Guo, Yandong Jo, Younghyun Yang, Sejong Kim, Seon Joo Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Liu, Jing Yoon, Kwangjin Jeon, Taegyun Akita, Kazutoshi Ooba, Takeru Ukita, Norimichi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu He, Dongliang Wu, Wenhao Ding, Yukang Li, Chao Li, Fu Wen, Shilei Li, Jianwei Yang, Fuzhi Yang, Huan Fu, Jianlong Kim, Byung-Hoon Baek, JaeHyun Ye, Jong Chul Fan, Yuchen Huang, Thomas S. Lee, Junyeop Lee, Bokyeung Min, Jungki Kim, Gwantae Lee, Kanghyu Park, Jaihyun Mykhailych, Mykola Zhong, Haoyu Shi, Yukai Yang, Xiaojun Yang, Zhijing Lin, Liang Zhao, Tongtong Peng, Jinjia Wang, Huibing Jin, Zhi Wu, Jiahao Chen, Yifu Shang, Chenming Zhang, Huanrong Min, Jeongki Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Computer Vision Lab ETH Zurich Switzerland OPPO Research Yonsei University Korea Republic of Facebook United States Xidian University China China SI Analytics Co. Ltd. 441 Expo-ro Yuseong-gu Daejeon34051 Korea Republic of Japan Co. Ltd China Baidu Inc China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Peking University China State Key Laboratory of Digital Publishing Technology Founder Group Shanghai Jiao Tong University China Microsoft Research Beijing China Korea Republic of Amazon Web Services University of Illinois at Urbana-Champaign United States Korea University Korea Republic of *** Ltd Guangdong University of Technology China Sun Yat-sen University China Dalian Maritime University China School of Intelligent Systems Engineering Sun Yat-sen University China College of Engineering Trivandrum India

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

引用

2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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