CN108604304A - 用于使来自源域的用于对象表示的深度模型适应于目标域的方法和*** - Google Patents

用于使来自源域的用于对象表示的深度模型适应于目标域的方法和*** Download PDF

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Publication number
CN108604304A
CN108604304A CN201680079452.0A CN201680079452A CN108604304A CN 108604304 A CN108604304 A CN 108604304A CN 201680079452 A CN201680079452 A CN 201680079452A CN 108604304 A CN108604304 A CN 108604304A
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China
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criterion
aiming field
depth model
feature
input picture
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Chinese (zh)
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汤晓鸥
张展鹏
罗平
吕健勤
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Sensetime Group Ltd
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Sensetime Group Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Image Analysis (AREA)
CN201680079452.0A 2016-01-20 2016-01-20 用于使来自源域的用于对象表示的深度模型适应于目标域的方法和*** Pending CN108604304A (zh)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/071501 WO2017124336A1 (fr) 2016-01-20 2016-01-20 Procédé et système d'adaptation d'un modèle profond permettant la représentation d'objet depuis un domaine source à un domaine cible

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CN108604304A true CN108604304A (zh) 2018-09-28

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CN201680079452.0A Pending CN108604304A (zh) 2016-01-20 2016-01-20 用于使来自源域的用于对象表示的深度模型适应于目标域的方法和***

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CN (1) CN108604304A (fr)
WO (1) WO2017124336A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112669247A (zh) * 2020-12-09 2021-04-16 深圳先进技术研究院 一种用于多任务医学图像合成的先验指导型网络
CN113255823A (zh) * 2021-06-15 2021-08-13 中国人民解放军国防科技大学 无监督域适应的方法及装置

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US11155809B2 (en) 2014-06-24 2021-10-26 Bio-Rad Laboratories, Inc. Digital PCR barcoding
CN113011568B (zh) * 2021-03-31 2024-06-21 华为技术有限公司 一种模型的训练方法、数据处理方法及设备
CN113159199B (zh) * 2021-04-27 2022-12-27 广东工业大学 一种基于结构特征增强和类中心匹配的跨域图像分类方法

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CN101840569A (zh) * 2010-03-19 2010-09-22 西安电子科技大学 基于迁移学习的投影寻踪高光谱图像分割方法
CN103354988A (zh) * 2011-01-31 2013-10-16 华为技术有限公司 用于计算穿过多个域的点对点标签交换路径的***和方法
CN103793718A (zh) * 2013-12-11 2014-05-14 台州学院 一种基于深度学习的人脸表情识别方法
CN104199023A (zh) * 2014-09-15 2014-12-10 南京大学 一种基于深度感知的rfid室内定位***及其工作方法
US20150055783A1 (en) * 2013-05-24 2015-02-26 University Of Maryland Statistical modelling, interpolation, measurement and anthropometry based prediction of head-related transfer functions
CN104616033A (zh) * 2015-02-13 2015-05-13 重庆大学 基于深度学习和支持向量机的滚动轴承故障诊断方法
CN105160866A (zh) * 2015-08-07 2015-12-16 浙江高速信息工程技术有限公司 一种基于深度学习神经网络结构的交通流预测方法

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* Cited by examiner, † Cited by third party
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CN102902966A (zh) * 2012-10-12 2013-01-30 大连理工大学 一种基于深度信赖网络的超分辨率人脸识别方法
CN104318215B (zh) * 2014-10-27 2017-09-19 中国科学院自动化研究所 一种基于域鲁棒卷积特征学习的交叉视角人脸识别方法

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101582813A (zh) * 2009-06-26 2009-11-18 西安电子科技大学 基于分布式迁移网络学习的入侵检测***及其方法
CN101840569A (zh) * 2010-03-19 2010-09-22 西安电子科技大学 基于迁移学习的投影寻踪高光谱图像分割方法
CN103354988A (zh) * 2011-01-31 2013-10-16 华为技术有限公司 用于计算穿过多个域的点对点标签交换路径的***和方法
US20150055783A1 (en) * 2013-05-24 2015-02-26 University Of Maryland Statistical modelling, interpolation, measurement and anthropometry based prediction of head-related transfer functions
CN103793718A (zh) * 2013-12-11 2014-05-14 台州学院 一种基于深度学习的人脸表情识别方法
CN104199023A (zh) * 2014-09-15 2014-12-10 南京大学 一种基于深度感知的rfid室内定位***及其工作方法
CN104616033A (zh) * 2015-02-13 2015-05-13 重庆大学 基于深度学习和支持向量机的滚动轴承故障诊断方法
CN105160866A (zh) * 2015-08-07 2015-12-16 浙江高速信息工程技术有限公司 一种基于深度学习神经网络结构的交通流预测方法

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112669247A (zh) * 2020-12-09 2021-04-16 深圳先进技术研究院 一种用于多任务医学图像合成的先验指导型网络
CN113255823A (zh) * 2021-06-15 2021-08-13 中国人民解放军国防科技大学 无监督域适应的方法及装置
CN113255823B (zh) * 2021-06-15 2021-11-05 中国人民解放军国防科技大学 无监督域适应的方法及装置

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Application publication date: 20180928