检索结果-内蒙古大学图书馆

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

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Robust Bayesian optimization with reinforcement learned acquisition functions

arXiv

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arXiv 2022年

作者： Liu, Zijing Qu, Xiyao Liu, Xuejun Lyu, Hongqiang Ministry of Industry and Information Technology Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Key Laboratory of Aerodynamic Noise Control State Key Laboratory of Aerodynamics Mianyang621000 China

In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima. Prevailing AFs usually rely on artificial experiences in terms of preferences for exploration or exploitation, which runs a risk of a computational waste or traps in local optima and resultant re-optimization. To address the crux, the idea of data-driven AF selection is proposed, and the sequential AF selection task is further formalized as a Markov decision process (MDP) and resort to powerful reinforcement learning (RL) technologies. Appropriate selection policy for AFs is learned from superior BO trajectories to balance between exploration and exploitation in real time, which is called reinforcement-learning-assisted Bayesian optimization (RLABO). Competitive and robust BO evaluations on five benchmark problems demonstrate RL's recognition of the implicit AF selection pattern and imply the proposal's potential practicality for intelligent AF selection as well as efficient optimization in expensive black-box problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Survey of Learning on Small Data: Generalization, Optimization, and Challenge

arXiv

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arXiv 2022年

作者： Cao, Xiaofeng Bu, Weixin Huang, Shengjun Zhang, Minling Tsang, Ivor W. Ong, Yew Soon Kwok, James T. The School of Artificial Intelligence Jilin University Changchun130012 China The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China The Australian Artificial Intelligence Institute University of Technology SydneyNSW2008 Australia Singapore The School of Computer Science and Engineering Nanyang Technological University Nanyang Avenue Singapore639798 Singapore Artificial Intelligence Scientist of A∗STAR Singapore The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI, which requires machines to recognize objectives and scenarios relying on small data as humans. A series of learning topics is going on this way such as active learning and few-shot learning. However, there are few theoretical guarantees for their generalization performance. Moreover, most of their settings are passive, that is, the label distribution is explicitly controlled by finite training resources from known distributions. This survey follows the agnostic active sampling theory under a PAC (Probably Approximately Correct) framework to analyze the generalization error and label complexity of learning on small data in model-agnostic supervised and unsupervised fashion. Considering multiple learning communities could produce small data representation and related topics have been well surveyed, we thus subjoin novel geometric representation perspectives for small data: the Euclidean and non-Euclidean (hyperbolic) mean, where the optimization solutions including the Euclidean gradients, non-Euclidean gradients, and Stein gradient are presented and discussed. Later, multiple learning communities that may be improved by learning on small data are summarized, which yield data-efficient representations, such as transfer learning, contrastive learning, graph representation learning. Meanwhile, we find that the meta-learning may provide effective parameter update policies for learning on small data. Then, we explore multiple challenging scenarios for small data, such as the weak supervision and multi-label. Finally, multiple data applications that may benefit from efficient small data representation are surveyed. Copyright © 2022, The Authors. All rights reserved.

关键词： Big data

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TIDE: Test Time Few Shot Object Detection

arXiv

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arXiv 2023年

作者： Li, Weikai Wei, Hongfeng Wu, Yanlai Yang, Jie Ruan, Yudi Li, Yuan Tang, Ying The School of Mathematics and Statistics Chongqing Jiaotong University Chongqing400074 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing210016 China China Science IntelliCloud Technology Co. Ltd. Anhui230000 China The China Science IntelliCloud Technology Co. Ltd. Anhui230000 China The School of Information Science and Technology Chongqing Jiaotong University Chongqing400074 China The Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States

Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain. Recent advances in FSOD focus on fine-tuning the base model based on a few objects via meta-learning or data augmentation. Despite their success, the majority of them are grounded with parametric readjustment to generalize on novel objects, which face considerable challenges in Industry 5.0, such as (i) a certain amount of fine-tuning time is required, and (ii) the parameters of the constructed model being unavailable due to the privilege protection, making the fine-tuning fail. Such constraints naturally limit its application in scenarios with real-time configuration requirements or within black-box settings. To tackle the challenges mentioned above, we formalize a novel FSOD task, referred to as Test TIme Few Shot DEtection (TIDE), where the model is un-tuned in the configuration procedure. To that end, we introduce an asymmetric architecture for learning a support-instance-guided dynamic category classifier. Further, a cross-attention module and a multi-scale resizer are provided to enhance the model performance. Experimental results on multiple few-shot object detection platforms reveal that the proposed TIDE significantly outperforms existing contemporary methods. The implementation codes are available at https://***/deku-0621/TIDE. © 2023, CC BY-NC-ND.

关键词： Object detection

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Deep Learning Radiomics Based on DCE-MRI Predicts Response of Conversion Therapy for Unresectable Hepatocellular Carcinoma

SSRN

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SSRN 2024年

作者： Liu, Han Zhang, Jicong Zou, Liwen Yin, Jing Wan, Peng Zhang, Yu Li, Zhengran Kong, Wentao Department of Ultrasound Nanjing Drum Tower Hospital Affiliated Hospital of Medical School Nanjing University Nanjing210002 China Department of Interventional Radiology the Third Affiliated Hospital of Sun Yat-sen University Guangzhou510630 China Department of Mathematics Nanjing University Nanjing210008 China Department of Pain Medicine Nanjing Jinling Hospital Affiliated Hospital of Medical School Nanjing University Nanjing210002 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China

Background: To develop and validate a deep learning radiomics nomogram (DLRN) for selecting unresectable patients with hepatocellular carcinoma (HCC) who can be transformed to resection within 4 cycles of conventional transarterial chemoembolization (c-TACE) based conversion therapy. Methods: Pretreatment contrast-enhanced MRI were retrospectively acquired in unresectable HCC patients who received c-TACE based conversion therapy from August 2017 to April 2023. The training cohort (TC) and internal validation cohort (IVC), comprising 190 and 81 patients, respectively, were randomly selected from Hospital 1. The external validation cohort (EVC) was comprised of 82 patients from Hospital 2 and 3. The deep learning radiomics signature (DLRS) and clinical model were constructed using a three-dimensional convolutional neural network and independent clinical factors, respectively. Multivariable logistic regression analysis was performed to develop the DLRN by integrating the DLRS with the clinical model. Prognostic information, including overall survival (OS) and progression-free survival (PFS), was analyzed by Kaplan-Meier curves and Cox regression. The performance of the models was assessed with respect to their discrimination and clinical utility. Findings: The DLRN yielded a satisfactory performance with areas under the receiver operating curve of 0.992 (95% CI, 0.982–1.000), 0.893 (95% CI, 0.822–0.965), and 0.929 (95% CI, 0.878–0.981) in the TC, IVC, and EVC, respectively. Furthermore, the DLRN exhibited superior performance compared to the clinical model in all cohorts (Delong P © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage

arXiv

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arXiv 2024年

作者： Yang, Ziyuan Kang, Ming Teoh, Andrew Beng Jin Gao, Chengrui Chen, Wen Zhang, Bob Zhang, Yi The College of Computer Science Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China The School of Cyber Science and Engineering Sichuan University Chengdu610065 China The School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of The College of Computer Science Sichuan University Chengdu610065 China The Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China The School of Cyber Science and Engineering Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China

In recent years, palmprints have been widely used for individual verification. The rich privacy information in palmprint data necessitates its protection to ensure security and privacy without sacrificing system performance. Existing systems often use cancelable technologies to protect templates, but these technologies ignore the potential risk of data leakage. Upon breaching the system and gaining access to the stored database, a hacker could easily manipulate the stored templates, compromising the security of the verification system. To address this issue, we propose a dual-level cancelable palmprint verification framework in this paper. Specifically, the raw template is initially encrypted using a competition hashing network with a first-level token, facilitating the end-to-end generation of cancelable templates. Different from previous works, the protected template undergoes further encryption to differentiate the second-level protected template from the first-level one. The system specifically creates a negative database (NDB) with the second-level token for dual-level protection during the enrollment stage. Reversing the NDB is NP-hard and a fine-grained algorithm for NDB generation is introduced to manage the noise and specified bits. During the verification stage, we propose an NDB matching algorithm based on matrix operation to accelerate the matching process of previous NDB methods caused by dictionary-based matching rules. This approach circumvents the need to store templates identical to those utilized for verification, reducing the risk of potential data leakage. Extensive experiments conducted on public palmprint datasets have confirmed the effectiveness and generality of the proposed framework. Upon acceptance of the paper, the code will be accessible at https://***/Deep-Imaging-Group/NPR. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT

arXiv

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arXiv 2020年

作者： Sun, Liang Mo, Zhanhao Yan, Fuhua Xia, Liming Shan, Fei Ding, Zhongxiang Shao, Wei Shi, Feng Yuan, Huan Jiang, Huiting Wu, Dijia Wei, Ying Gao, Yaozong Gao, Wanchun Sui, He Zhang, Daoqiang Shen, Dinggang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Department of Radiology Third Hospital of Jilin University Changchun China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology WuhanHubei China Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai China Department of Radiology Hangzhou First Peoples Hospital Zhejiang University School of Medicine HangzhouZhejiang China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Department of Radiology Qianjiang Central Hospital Jishou University School of Medicine Chongqing China

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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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

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Corrigendum to “Introducing a Chaotic Component in the Control System of Soil Respiration”

引用

Complexity 2022年第1期2022卷

作者： Peng An Wen-Feng Wang Xi Chen Jing Qian Yunzhu Pan Laboratory of Pattern Analysis and Machine Intelligence School of Electronic and Information Engineering Ningbo University of Technology Ningbo 315211 *** Research Institute of Intelligent Engineering and Data Applications School of Electronic and Electrical Engineering Shanghai Institute of Technology Shanghai 200235 *** State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 *** University of Chinese Academy of Sciences Beijing 100049 *** Sino-Belgian Joint Laboratory of Geo-information Urumqi 830011 China CAS Research Centre for Ecology and Environment of Central Asia Urumqi 830011 *** Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 *** School of Management Cranfield University Cranfield MK43 0AL UKcranfield.ac.uk

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Correction: The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment

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Translational psychiatry 2024年第1期14卷 331页

作者： Haifeng Chen Jingxian Xu Weikai Li Zheqi Hu Zhihong Ke Ruomeng Qin Yun Xu Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine Nanjing University of Chinese Medicine Nanjing China. Department of Neurology Nanjing Drum Tower Hospital Affiliated Hospital of Medical School Nanjing University Nanjing China. Jiangsu Key Laboratory of Molecular Medicine Medical School of Nanjing University Nanjing China. Jiangsu Province Stroke Center for Diagnosis and Therapy Nanjing China. Nanjing Neuropsychiatry Clinic Medical Center Nanjing China. School of Mathematics and Statistics Chongqing Jiaotong University Chongqing China. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China. Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine Nanjing University of Chinese Medicine Nanjing China. xuyun20042001@***. Department of Neurology Nanjing Drum Tower Hospital Affiliated Hospital of Medical School Nanjing University Nanjing China. xuyun20042001@***. Jiangsu Key Laboratory of Molecular Medicine Medical School of Nanjing University Nanjing China. xuyun20042001@***. Jiangsu Province Stroke Center for Diagnosis and Therapy Nanjing China. xuyun20042001@***. Nanjing Neuropsychiatry Clinic Medical Center Nanjing China. xuyun20042001@***.

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