US 11,861,471 B2
Computer vision image feature identification via multi-label few-shot model
Hui Peng Hu, Berkeley, CA (US); and Ramesh Sridharan, Oakland, CA (US)
Assigned to DST Technologies, Inc., Kansas City, MO (US)
Filed by DST Technologies, Inc., Kansas City, MO (US)
Filed on Dec. 16, 2021, as Appl. No. 17/644,774.
Application 17/644,774 is a continuation of application No. 16/678,982, filed on Nov. 8, 2019, granted, now 11,238,275.
Prior Publication US 2022/0172500 A1, Jun. 2, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06K 9/00 (2022.01); G06N 20/00 (2019.01); G06F 17/16 (2006.01); G06V 30/414 (2022.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06V 10/75 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01); G06V 10/40 (2022.01)
CPC G06N 20/00 (2019.01) [G06F 17/16 (2013.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06V 10/40 (2022.01); G06V 10/75 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06V 30/414 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
training a few-shot model with a few-shot data set, the few-shot data set including a number of images, each image of the number of images having a combination of graphic features, wherein the few-shot data set does not include every combination of the graphic features;
generating a matrix of graphic features of the few-shot data set;
receiving a query image by the few-shot model, the query image including a query combination of graphic features using graphic features found within the few-shot data set and represented by the matrix of graphic features; and
identifying graphic features present in the query image via the few-shot model based on the matrix of graphic features.