EP3500978A4 - Method and apparatus for zero-shot learning - Google Patents

Method and apparatus for zero-shot learning Download PDF

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Publication number
EP3500978A4
EP3500978A4 EP16913114.1A EP16913114A EP3500978A4 EP 3500978 A4 EP3500978 A4 EP 3500978A4 EP 16913114 A EP16913114 A EP 16913114A EP 3500978 A4 EP3500978 A4 EP 3500978A4
Authority
EP
European Patent Office
Prior art keywords
zero
shot learning
shot
learning
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.)
Withdrawn
Application number
EP16913114.1A
Other languages
German (de)
French (fr)
Other versions
EP3500978A1 (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
Original Assignee
Nokia Technologies Oy
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nokia Technologies Oy filed Critical Nokia Technologies Oy
Publication of EP3500978A1 publication Critical patent/EP3500978A1/en
Publication of EP3500978A4 publication Critical patent/EP3500978A4/en
Withdrawn legal-status Critical Current

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Classifications

    • 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
    • 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
    • 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
    • G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/28—Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/20—Scenes; Scene-specific elements in augmented reality 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
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Image Analysis (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
EP16913114.1A 2016-08-16 2016-08-16 Method and apparatus for zero-shot learning Withdrawn EP3500978A4 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/095512 WO2018032354A1 (en) 2016-08-16 2016-08-16 Method and apparatus for zero-shot learning

Publications (2)

Publication Number Publication Date
EP3500978A1 EP3500978A1 (en) 2019-06-26
EP3500978A4 true EP3500978A4 (en) 2020-01-22

Family

ID=61196222

Family Applications (1)

Application Number Title Priority Date Filing Date
EP16913114.1A Withdrawn EP3500978A4 (en) 2016-08-16 2016-08-16 Method and apparatus for zero-shot learning

Country Status (3)

Country Link
EP (1) EP3500978A4 (en)
CN (1) CN109643384A (en)
WO (1) WO2018032354A1 (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110580501B (en) * 2019-08-20 2023-04-25 天津大学 Zero sample image classification method based on variational self-coding countermeasure network
CN112418257B (en) * 2019-08-22 2023-04-18 四川大学 Effective zero sample learning method based on potential visual attribute mining
CN110826638B (en) * 2019-11-12 2023-04-18 福州大学 Zero sample image classification model based on repeated attention network and method thereof
CN111914903B (en) * 2020-07-08 2022-10-25 西安交通大学 Generalized zero sample target classification method and device based on external distribution sample detection and related equipment
CN112380374B (en) * 2020-10-23 2022-11-18 华南理工大学 Zero sample image classification method based on semantic expansion
CN114627312B (en) * 2022-05-17 2022-09-06 中国科学技术大学 Zero sample image classification method, system, equipment and storage medium
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
CN116109877B (en) * 2023-04-07 2023-06-20 中国科学技术大学 Combined zero-sample image classification method, system, equipment and storage 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

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103164713B (en) * 2011-12-12 2016-04-06 阿里巴巴集团控股有限公司 Image classification method and device
US20130251340A1 (en) * 2012-03-21 2013-09-26 Wei Jiang Video concept classification using temporally-correlated grouplets
CN103400160B (en) * 2013-08-20 2017-03-01 中国科学院自动化研究所 A kind of zero training sample Activity recognition method
CN103646256A (en) * 2013-12-17 2014-03-19 上海电机学院 Image characteristic sparse reconstruction based image classification method
CN105184260B (en) * 2015-09-10 2019-03-08 北京大学 A kind of image characteristic extracting method and pedestrian detection method and device
CN105512679A (en) * 2015-12-02 2016-04-20 天津大学 Zero sample classification method based on extreme learning machine
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
CN105740879B (en) * 2016-01-15 2019-05-21 天津大学 The zero sample image classification method based on multi-modal discriminant analysis

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
FARHADI A ET AL: "Describing objects by their attributes", 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION : CVPR 2009 ; MIAMI [BEACH], FLORIDA, USA, 20 - 25 JUNE 2009, IEEE, PISCATAWAY, NJ, 20 June 2009 (2009-06-20), pages 1778 - 1785, XP031607299, ISBN: 978-1-4244-3992-8 *
FU ZHENYONG ET AL: "Zero-shot object recognition by semantic manifold distance", 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), IEEE, 7 June 2015 (2015-06-07), pages 2635 - 2644, XP032793709, DOI: 10.1109/CVPR.2015.7298879 *
LIU MINGXIA ET AL: "Attribute relation learning for zero-shot classification", NEUROCOMPUTING, ELSEVIER, AMSTERDAM, NL, vol. 139, 3 April 2014 (2014-04-03), pages 34 - 46, XP029024275, ISSN: 0925-2312, DOI: 10.1016/J.NEUCOM.2013.09.056 *
See also references of WO2018032354A1 *

Also Published As

Publication number Publication date
CN109643384A (en) 2019-04-16
EP3500978A1 (en) 2019-06-26
WO2018032354A1 (en) 2018-02-22

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