Patent application EP22157969A (EP), filed 2022-02-22
Publication number: EP4105891A1
Title: IMAGE RECOGNITION SUPPORT APPARATUS, IMAGE RECOGNITION SUPPORT METHOD, AND IMAGE RECOGNITION SUPPORT PROGRAM
Abstract: The invention supports creation of models for recognizing attributes in an image with high accuracy. An image recognition support apparatus includes an image input unit configured to acquire an image, a pseudo label generation unit configured to recognize the acquired image based on a plurality of types of image recognition models and output recognition information, and generate pseudo labels indicating attributes of the acquired image based on the output recognition information, and a new label generation unit configured to generate new labels based on the generated pseudo labels.
IPC codes: G06N   3/    04            A I
G06V  10/    44            A I
G06V  10/   774            A I
G06V  10/   776            A I
G06V  10/    80            A I
G06V  10/    82            A I
G06V  20/    13            A I
G06N  20/    00            A I
Claims: number: 1
text: An image recognition support apparatus comprising:
number: 2
text: The image recognition support apparatus according to claim 1, wherein
number: 3
text: The image recognition support apparatus according to claim 2, wherein
the pseudo label generation unit (202, 604) generates the integrated recognition information based on the recognition information on the attributes output from the plurality of classifiers and predetermined weighting coefficients associated with the corresponding classifiers and the corresponding attributes.
number: 4
text: The image recognition support apparatus according to claim 2, wherein
the pseudo label generation unit (202, 604) sets, as the characteristic values relating to the attributes of the image, frequencies at which the attributes occur in the image.
number: 5
text: The image recognition support apparatus according to claim 2, wherein
the pseudo label generation unit (202, 604) sets, as the characteristic values relating to the attributes of the image, information indicating certainty of recognition of the attributes performed by the classifiers.
number: 6
text: The image recognition support apparatus according to claim 1, further comprising:
a storage unit configured to store the acquired image, the pseudo labels of the image, and the new labels of the image in association with each other.
number: 7
text: The image recognition support apparatus according to claim 1, wherein
the image includes an SAR image or a CG image.
number: 8
text: The image recognition support apparatus according to claim 2, further comprising:
a display unit configured to display the output recognition information on the attributes, and transmit information on a predetermined operation to a predetermined terminal associated with the attributes based on a designation from a user.
number: 9
text: The image recognition support apparatus according to claim 8, wherein
the display unit displays recognition information regarding an attribute for which the integrated recognition information relating to the pseudo labels indicates a predetermined value or a predetermined range.
number: 10
text: The image recognition support apparatus according to claim 8, wherein
the display unit displays information on the new labels corresponding to the attributes, and receives a change in the information on the new labels from the user.
number: 11
text: The image recognition support apparatus according to claim 2, further comprising:
an image recognition model generation unit configured to, by learning relations between a plurality of the acquired images and the new labels generated corresponding to each of the plurality of images, generate a learned model that receives an image and outputs recognition information on attributes of the image.
number: 12
text: The image recognition support apparatus according to claim 2, wherein
number: 13
text: An image recognition support method for an information processing apparatus to perform:
number: 14
text: An image recognition support program that causes an information processing apparatus to perform:
Office action: search_report, dated 2022-12-21
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: JP2019046095A
ref_type: patent
severity: background
ref_id: US2021089964A1
ref_type: patent
severity: background
ref_id: - HAO-CHIANG SHAO ET AL: "Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction", ARXIV.ORG, 13 March 2021 (2021-03-13), Ithaca, pages 1 - 12, XP055954057, Retrieved from the Internet <URL:https://arxiv.org/pdf/2103.07641.pdf> [retrieved on 20220823]
ref_type: npl
severity: background
ref_id: - MO JIANWEN ET AL: "Weighted Pseudo Labeled Data and Mutual Learning for Semi-Supervised Classification", IEEE ACCESS, IEEE, USA, vol. 9, 2 March 2021 (2021-03-02), pages 36522 - 36534, XP011842866, DOI: 10.1109/ACCESS.2021.3063176
ref_type: npl
severity: background
ref_id: - TANAKA DAIKI ET AL: "Joint Optimization Framework for Learning with Noisy Labels", 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, IEEE, 18 June 2018 (2018-06-18), pages 5552 - 5560, XP033473469, DOI: 10.1109/CVPR.2018.00582
ref_type: npl
severity: background
ref_id: - OKAZAKI SOICHIRO ET AL: "Hitachi at TRECVID DSDI 2020", TREC VIDEO RETRIEVAL EVALUATION, 21 April 2021 (2021-04-21), pages 1 - 8, XP055954169, Retrieved from the Internet <URL:https://www-nlpir.nist.gov/projects/tvpubs/tv20.papers/vas.pdf> [retrieved on 20220823]
ref_type: npl
severity: background
ref_id: - DONG-HYUN LEE: "Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks", ICML 2013 WORKSHOP : CHALLENGES IN REPRESENTATION LEARNING (WREPL), 16 June 2013 (2013-06-16), Atlanta, Georgia, USA, XP055716966, Retrieved from the Internet <URL:http://deeplearning.net/wp-content/uploads/2013/03/pseudo_label_final.pdf> [retrieved on 20200721]
ref_type: npl
severity: background
ref_id: US2021089964
ref_type: patent
severity: novelty_destroying
ref_id: - Hao-Chiang Shao ET AL, "Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction", arXiv.org, Ithaca, (20210313), pages 1 - 12, URL: https://arxiv.org/pdf/2103.07641.pdf, (20220823), XP055954057 [X] 1-3,5,6,11,13,14 * pages  1-7 * [I] 4,7-10
ref_type: npl
severity: novelty_destroying
ref_id: - MO JIANWEN ET AL, "Weighted Pseudo Labeled Data and Mutual Learning for Semi-Supervised Classification", IEEE ACCESS, IEEE, USA, (20210302), vol. 9, doi:10.1109/ACCESS.2021.3063176, pages 36522 - 36534, XP011842866 [X] 1,2,13,14 * page  2652  - page  36527 *
ref_type: npl
severity: novelty_destroying
ref_id: - TANAKA DAIKI ET AL, "Joint Optimization Framework for Learning with Noisy Labels", 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, IEEE, (20180618), doi:10.1109/CVPR.2018.00582, pages 5552 - 5560, XP033473469 [X] 1,2,13,14 * page  5552  - page  5556 *
ref_type: npl
severity: novelty_destroying
ref_id: - Okazaki Soichiro ET AL, "Hitachi at TRECVID DSDI 2020", TREC Video Retrieval Evaluation, (20210421), pages 1 - 8, URL: https://www-nlpir.nist.gov/projects/tvpubs/tv20.papers/vas.pdf, (20220823), XP055954169 [A] 12 * pages  1-3 *
ref_type: npl
severity: background
ref_id: - Dong-Hyun Lee, "Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks", ICML 2013 Workshop : Challenges in Representation Learning (WREPL), Atlanta, Georgia, USA, (20130616), URL: http://deeplearning.net/wp-content/uploads/2013/03/pseudo_label_final.pdf, (20200721), XP055716966 [A] 1-14 * Sect. 2.4 *
ref_type: npl
severity: background
Final disposition: pending (2022-12-21)