检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuyun Wen Daoqiang Zhang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of Education Shanghai China

ISBN: (纸本)9781665429825

Detecting the time-varying community structure of brain functional network is very important to reveal dynamic properties of the human brain. Although several community detection methods have been proposed, they are limited in real application due to their poor performance in large dynamic network and difficulty in parameter setting without prior knowledge. To address these problems, this paper proposes a novel dynamic community detection method for the brain network based on random walk, named as ML-RW. This method uses local community discovery instead of global community detection to improve its ability to deal with large dynamic networks. Specifically, ML-RW first selects a query brain region and sends out multiple random walkers starting from this region to explore local community structures of all networks in dynamic network simultaneously. It updates the visiting probability vector of each walker by aggregating the transition probabilities from itself and two temporally adjacent networks. Since the influence strength from one network to another is adaptively tuned according to the relevance of visiting histories of two networks, ML-RW could guarantee the temporal smoothness of the detected modular structure without introducing the hyper-parameter and thus avoids the problem of parameter setting in existing methods. Experiments on two public real neuroimaging datasets demonstrate that our proposed method has more potential to capture subtle community variations in the brain region, stronger ability to discover biomarkers for brain diseases, and higher test-retest reliability than the conventional method.

关键词： Neuroimaging Knowledge engineering Conferences Biomarkers History Reliability Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Partial Multi-label Learning

Semi-Supervised Partial Multi-label Learning

引用

IEEE International Conference on Data Mining (ICDM)

作者： Ming-Kun Xie Sheng-Jun Huang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIlT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Partial multi-label learning (PML) deals with problems where each instance is associated with a candidate label set, which contains multiple relevant labels and some noisy labels. In many real-world scenarios, it is impractical to annotate all examples for a huge-size dataset. Instead, a more common case is that only a small set of the data are annotated with partial labels, while most data are unlabeled. In this paper, we formalize such problems as a new learning framework called Semi-Supervised Partial Multi-label Learning (SSPML). To solve the SSPML problem, a latent label variable is introduced for each example as the low-dimensional embedding of the feature space. On one hand, label variables are recovered by encouraging consistent similarity measurement between the feature space and the label space; on the other hand, the similarities are adaptively updated based on the feedback from the label space. Meanwhile, the multi-label classifier is jointly trained under the supervision of label variables. Extensive experiments on multiple datasets from various real-world tasks validate the effectiveness of the proposed approach.

关键词： Weight measurement Measurement uncertainty Prediction algorithms Extraterrestrial measurements Noise measurement Data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

Deep robust multilevel semantic cross-modal hashing

arXiv

引用

arXiv 2020年

作者： Song, Ge Zhao, Jun Tan, Xiaoyang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Miit Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanyang Technological University

Hashing based cross-modal retrieval has rec made significant progress. But straightfor embedding data from different modalities in joint Hamming space will inevitably produce codes due to the intrinsic modality discrep and noises. We present a novel Robust M level Semantic Hashing (RMSH) for more a rate cross-modal retrieval. It seeks to pres fine-grained similarity among data with rich mantics, while explicitly require distances bet dissimilar points to be larger than a specific v for strong robustness. For this, we give an fective bound of this value based on the information coding-theoretic analysis, and the above goals are embodied into a margin-adaptive triplet loss. Furthermore, we introduce pseudo-codes via fusing multiple hash codes to explore seldom-seen semantics, alleviating the sparsity problem of similarity information. Experiments on three benchmarks show the validity of the derived bounds, and our method achieves state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Real-time human cross-race aging-related face appearance detection with deep convolution architecture

Real-time human cross-race aging-related face appearance det...

引用

作者： Tian, Qing Zhang, Wenqiang Mao, Junxiang Yin, Hujun School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

Human age estimation (AE) is an emerging research topic in computer vision and machine learning and has attracted increasing amount of research due its wide potential applications. In the process of human aging, facial appearances change from glabrous to crinkly similarly across all races, from European, Hispanic and African to Asian. To specially explore the relationships between aging and facial appearances across races, this paper is devoted to determining the correspondence between facial aging and facial appearances. Specifically, we first extract appearance vector features from facial images with their spatial structure preserved. Then, we propose to select the aging-related features shared by different races to explore their aging-related common facial regions, while removing redundant features. Thirdly, we improve the proposed model by incorporating potential cross-race relationships in an automated learning manner. Additionally, we extend our model with deep convolution architecture. Finally, we evaluate the proposed methodologies on a large face aging database with real-time efficiency. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Methods to Improve Multi-Step Time Series Prediction

Methods to Improve Multi-Step Time Series Prediction

引用

2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Koesdwiady, Arief Khatib, Alaa El Karray, Fakhri Center for Pattern Analysis and Machine Intelligence University of Waterloo C-Ontario Canada

ISBN: (纸本)9781509060146

Multi-step time series prediction is known to suffer from increasing performance degradation the farther in the future the predictions are made. In this paper, we introduce two approaches to address this weakness in recursive and multioutput prediction models. In particular, we present a model that allows recursive prediction approaches to take into account the time-step index when making predictions. In addition, we propose a conditional generative adversarial network-based data augmentation model to improve prediction performance in multioutput models. We show on real-world time series datasets that the two methods improve on multi-step time series prediction in recursive and multi-output models, respectively. © 2018 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

AADL and Modelica model combination and model conversion based on CPS 20

AADL and Modelica model combination and model conversion bas...

引用

Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Yifeng Zhu Zining Cao Fujun Wang Weiwei Lu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Computer Science and Technology Collaborative Innovation Center of Novel Software Technology and Industrialization MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics and Science and Technology on Electro-optic Control Laboratory Luoyang China Science and Technology on Electro-optic Control Laboratory Luoyang China

ISBN: (纸本)9781450387811

Cyber-Physical System (CPS), which realizes the close integration of physical resources and information resources, is a distributed and asynchronous dynamic hybrid system running in different time and space. In this paper, we use AADL to model information system and use Modelica to model physical system. However, they are all semi-formal models such that we cannot do future work about formal verification. To overcome this defect, we convert them to formal models like automaton through conversion algorithms and rules. Due to the fact of probabilistic events and resource consumption in CPS, this paper adopts the weighted probabilistic hybrid automaton as formal model, and adds the properties of probability and weight to the behavior annex of AADL. Then we conduct the rule and algorithm of the model conversion. In order to ensure consistency of further work about model checking, the paper prove the equivalence of the two models by bisimulation. Finally, the capability of modeling of the extended modeling language of AADL and the significance of conversion to automaton is illustrated by the aircraft system.

关键词： Cyber-Physical System model conversion weighted probabilistic hybrid automaton AADL behavior annex bisimulation

来源：评论

学校读者我要写书评

暂无评论

Learning Calibrated-Guidance for Object Detection in Aerial Images

arXiv

引用

arXiv 2021年

作者： Wei, Zongqi Liang, Dong Zhang, Dong Zhang, Liyan Geng, Qixiang Wei, Mingqiang Zhou, Huiyu The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Object detection is one of the most fundamental yet challenging research topics in the domain of computer vision. Recently, the study on this topic in aerial images has made tremendous progress. However, complex background and worse imaging quality are obvious problems in aerial object detection. Most state-of-the-art approaches tend to develop elaborate attention mechanisms for the space-time feature calibrations with arduous computational complexity, while surprisingly ignoring the importance of feature calibrations in channel-wise. In this work, we propose a simple yet effective Calibrated-Guidance (CG) scheme to enhance channel communications in a feature transformer fashion, which can adaptively determine the calibration weights for each channel based on the global feature affinity correlations. Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance. Then, re-representing each channel by aggregating all the channels weighted together via the guidance operation. Our CG is a general module that can be plugged into any deep neural networks, which is named as CG-Net. To demonstrate its effectiveness and efficiency, extensive experiments are carried out on both oriented object detection task and horizontal object detection task in aerial images. Experimental results on two challenging benchmarks (i.e., DOTA and HRSC2016) demonstrate that our CG-Net can achieve the new state-of-the-art performance in accuracy with a fair computational overhead. The source code has been open sourced at https://***/WeiZongqi/CG-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Anchor retouching via model interaction for robust object detection in aerial images

arXiv

引用

arXiv 2021年

作者： Liang, Dong Geng, Qixiang Wei, Zongqi Vorontsov, Dmitry A. Kim, Ekaterina L. Wei, Mingqiang Zhou, Huiyu The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China The National Research Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

Object detection has made tremendous strides in computer vision. Small object detection with appearance degradation is a prominent challenge, especially for aerial observations. To collect sufficient positive/negative samples for heuristic training, most object detectors preset region anchors in order to calculate Intersection-over-Union (IoU) against the groundtruthed data. In this case, small objects are frequently abandoned or mislabeled. In this paper, we present an effective Dynamic Enhancement Anchor (DEA) network to construct a novel training sample generator. Different from the other state-of-the-art techniques, the proposed network leverages a sample discriminator to realize interactive sample screening between an anchor-based unit and an anchor-free unit to generate eligible samples. Besides, multi-task joint training with a conservative anchor-based inference scheme enhances the performance of the proposed model while reducing computational complexity. The proposed scheme supports both oriented and horizontal object detection tasks. Extensive experiments on two challenging aerial benchmarks (i.e., DOTA and HRSC2016) indicate that our method achieves state-of-the-art performance in accuracy with moderate inference speed and computational overhead for training. On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0.40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3.08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone. Besides, our DEA-Net which integrated with the baseline of ReDet achieves the state-of-the-art performance by 80.37%. On HRSC2016, it surpasses the previous best model by 1.1% using only 3 horizontal anchors. The source code and the training set are made publicly available at: https://***/QxGeng/DEA-Net. © 2021, CC BY.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Continual Learning in the Presence of Repetition

arXiv

引用

arXiv 2024年

作者： Hemati, Hamed Pellegrini, Lorenzo Duan, Xiaotian Zhao, Zixuan Xia, Fangfang Masana, Marc Tscheschner, Benedikt Veas, Eduardo Zheng, Yuxiang Zhao, Shiji Li, Shao-Yuan Huang, Sheng-Jun Lomonaco, Vincenzo van de Ven, Gido M. Institute for Computer Science University of St. Gallen Rosenbergstrasse 30 St. Gallen9000 Switzerland Department of Computer Science University of Bologna Via dell’Università 50 Cesena47521 Italy The University of Chicago 5801 S Ellis Ave Chicago60637 United States Argonne National Laboratory 9700 S Cass Ave Lemont60439 United States Graz University of Technology Rechbauerstraße 12 Graz8010 Austria TU Graz - SAL Dependable Embedded Systems Lab Silicon Austria Labs Graz8010 Austria Know-Center GmbH Sandgasse 36/4 Graz8010 Austria MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science University of Pisa Piano Secondo Largo Bruno Pontecorvo 3 Pisa56127 Italy Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 Leuven3001 Belgium

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design. © 2024, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：