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检索条件"机构=Miit Key Laboratory of Pattern Analysis and Machine Intelligence"
231 条 记 录,以下是161-170 订阅
排序:
Learning from Positive and Unlabeled Data with Augmented Classes
SSRN
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SSRN 2023年
作者: Li, Zhongnian Yang, Liutao Ma, Zhongchen Sun, Tongfeng Xu, Xinzheng Zhang, Daoqiang School of Computer Science and Technology China University of Ming and Technogy Jiangsu Xuzhou221000 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210000 China School of Computer Science and communications Engineering Jiangsu University Jiangsu Zhenjiang212013 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu Nanjing210000 China
Positive Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled data, which is utilized in many real-world scenarios. However, existing PU learning algorithms cannot deal with the r... 详细信息
来源: 评论
Better safe than sorry: preventing delusive adversaries with adversarial training  21
Better safe than sorry: preventing delusive adversaries with...
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Proceedings of the 35th International Conference on Neural Information Processing Systems
作者: Lue Tao Lei Feng Jinfeng Yi Sheng-Jun Huang Songcan Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics and MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science Chongqing University JD AI Research
Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing this malicious attack as finding the...
来源: 评论
A Similarity-based Framework for Classification Task
arXiv
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arXiv 2022年
作者: Ma, Zhongchen Chen, Songcan The School of Computer Science & communications Engineering Jiangsu University The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Zhenjiang212013 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China
—Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classificatio... 详细信息
来源: 评论
Centralized Feature Pyramid for Object Detection
arXiv
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arXiv 2022年
作者: Quan, Yu Zhang, Dong Zhang, Liyan Tang, Jinhui The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China 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
Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. However, the existing methods exorbitantly concentrate on the inter-layer feature interactions but... 详细信息
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Coarse-to-fine Foreground Segmentation based on Co-occurrence Pixel-Block and Spatio-Temporal Attention Model
Coarse-to-fine Foreground Segmentation based on Co-occurrenc...
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International Conference on pattern Recognition
作者: Dong Liang Xinyu Liu 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 Nanjing China
Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other ha... 详细信息
来源: 评论
ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection
ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surf...
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International Conference on pattern Recognition
作者: Junting Fang Xiaoyang Tan Yuhui Wang 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 Nanjing China
Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definiti... 详细信息
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Improving model robustness by adaptively correcting perturbation levels with active queries
arXiv
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arXiv 2021年
作者: Ning, Kun-Peng Tao, Lue Chen, Songcan Huang, Sheng-Jun 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
In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improving the model robustness by training w... 详细信息
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Deep Learning in Palmprint Recognition-A Comprehensive Survey
arXiv
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arXiv 2025年
作者: Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of
Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as t... 详细信息
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Open-set label noise can improve robustness against inherent label noise
arXiv
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arXiv 2021年
作者: Wei, Hongxin Tao, Lue Xie, Renchunzi An, Bo School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT key Laboratory of Pattern Analysis and Machine Intelligence China
Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-se... 详细信息
来源: 评论
Open-set label noise can improve robustness against inherent label noise  21
Open-set label noise can improve robustness against inherent...
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Proceedings of the 35th International Conference on Neural Information Processing Systems
作者: Hongxin Wei Lue Tao Renchunzi Xie Bo An School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China and MIIT key Laboratory of Pattern Analysis and Machine Intelligence China
Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-se...
来源: 评论