EP3593284A4 - A transductive and/or adaptive max margin zero-shot learning method and system - Google Patents

A transductive and/or adaptive max margin zero-shot learning method and system Download PDF

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Publication number
EP3593284A4
EP3593284A4 EP17899880.3A EP17899880A EP3593284A4 EP 3593284 A4 EP3593284 A4 EP 3593284A4 EP 17899880 A EP17899880 A EP 17899880A EP 3593284 A4 EP3593284 A4 EP 3593284A4
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EP
European Patent Office
Prior art keywords
transductive
learning method
shot learning
max margin
adaptive max
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP17899880.3A
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German (de)
French (fr)
Other versions
EP3593284A1 (en
Inventor
Yunlong YU
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nokia Technologies Oy
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Nokia Technologies Oy
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Filing date
Publication date
Application filed by Nokia Technologies Oy filed Critical Nokia Technologies Oy
Publication of EP3593284A1 publication Critical patent/EP3593284A1/en
Publication of EP3593284A4 publication Critical patent/EP3593284A4/en
Pending legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G06F18/21355—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis nonlinear criteria, e.g. embedding a manifold in a Euclidean space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06F18/2155—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • 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
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Multimedia (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
EP17899880.3A 2017-03-06 2017-03-06 A transductive and/or adaptive max margin zero-shot learning method and system Pending EP3593284A4 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/075764 WO2018161217A1 (en) 2017-03-06 2017-03-06 A transductive and/or adaptive max margin zero-shot learning method and system

Publications (2)

Publication Number Publication Date
EP3593284A1 EP3593284A1 (en) 2020-01-15
EP3593284A4 true EP3593284A4 (en) 2021-03-10

Family

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Family Applications (1)

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EP17899880.3A Pending EP3593284A4 (en) 2017-03-06 2017-03-06 A transductive and/or adaptive max margin zero-shot learning method and system

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EP (1) EP3593284A4 (en)
CN (1) CN110431565B (en)
WO (1) WO2018161217A1 (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109598279B (en) * 2018-09-27 2023-04-25 天津大学 Zero sample learning method based on self-coding countermeasure generation network
CN109582960B (en) * 2018-11-27 2020-11-24 上海交通大学 Zero example learning method based on structured association semantic embedding
EP3751467A1 (en) * 2019-06-14 2020-12-16 Robert Bosch GmbH A machine learning system
CN113763744A (en) * 2020-06-02 2021-12-07 荷兰移动驱动器公司 Parking position reminding method and vehicle-mounted device
CN111914872B (en) * 2020-06-04 2024-02-02 西安理工大学 Zero sample image classification method with label and semantic self-coding fused
CN115424096B (en) * 2022-11-08 2023-01-31 南京信息工程大学 Multi-view zero-sample image identification method
CN116051909B (en) * 2023-03-06 2023-06-16 中国科学技术大学 Direct push zero-order learning unseen picture classification method, device and medium
CN117541882B (en) * 2024-01-05 2024-04-19 南京信息工程大学 Instance-based multi-view vision fusion transduction type zero sample classification method
CN117893743B (en) * 2024-03-18 2024-05-31 山东军地信息技术集团有限公司 Zero sample target detection method based on channel weighting and double-comparison learning

Citations (1)

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Publication number Priority date Publication date Assignee Title
US20160253597A1 (en) * 2015-02-27 2016-09-01 Xerox Corporation Content-aware domain adaptation for cross-domain classification

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CN101179860B (en) * 2007-12-05 2011-03-16 中兴通讯股份有限公司 ZC sequence ranking method and apparatus for random access channel
US10331976B2 (en) * 2013-06-21 2019-06-25 Xerox Corporation Label-embedding view of attribute-based recognition
WO2016145379A1 (en) * 2015-03-12 2016-09-15 William Marsh Rice University Automated Compilation of Probabilistic Task Description into Executable Neural Network Specification
CN105512679A (en) * 2015-12-02 2016-04-20 天津大学 Zero sample classification method based on extreme learning machine
CN105701504B (en) * 2016-01-08 2019-09-13 天津大学 Multi-modal manifold embedding grammar for zero sample learning
CN105701514B (en) * 2016-01-15 2019-05-21 天津大学 A method of the multi-modal canonical correlation analysis for zero sample classification
CN105718940B (en) * 2016-01-15 2019-03-29 天津大学 The zero sample image classification method based on factorial analysis between multiple groups
CN105740888A (en) * 2016-01-26 2016-07-06 天津大学 Joint embedded model for zero sample learning
CN106096661B (en) * 2016-06-24 2019-03-01 中国科学院电子学研究所苏州研究院 The zero sample image classification method based on relative priority random forest
CN106203472B (en) * 2016-06-27 2019-04-02 中国矿业大学 A kind of zero sample image classification method based on the direct prediction model of mixed attributes
CN106203483B (en) * 2016-06-29 2019-06-11 天津大学 A kind of zero sample image classification method based on semantic related multi-modal mapping method
CN106250925B (en) * 2016-07-25 2019-06-11 天津大学 A kind of zero Sample video classification method based on improved canonical correlation analysis

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160253597A1 (en) * 2015-02-27 2016-09-01 Xerox Corporation Content-aware domain adaptation for cross-domain classification

Also Published As

Publication number Publication date
CN110431565B (en) 2023-06-20
EP3593284A1 (en) 2020-01-15
CN110431565A (en) 2019-11-08
WO2018161217A1 (en) 2018-09-13

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