US 12,136,260 B2
Adaptive learning for semantic segmentation
Nader Salman, Houston, TX (US); and Matthias Cremieux, Paris (FR)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Appl. No. 18/702,274
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
PCT Filed Oct. 27, 2022, PCT No. PCT/US2022/047968
§ 371(c)(1), (2) Date Apr. 17, 2024,
PCT Pub. No. WO2023/076438, PCT Pub. Date May 4, 2023.
Claims priority of provisional application 63/272,438, filed on Oct. 27, 2021.
Prior Publication US 2024/0331368 A1, Oct. 3, 2024
Int. Cl. G06V 10/778 (2022.01); G06N 3/09 (2023.01); G06N 3/091 (2023.01); G06V 10/26 (2022.01); G06V 10/762 (2022.01); G06V 10/774 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/7788 (2022.01) [G06N 3/09 (2023.01); G06N 3/091 (2023.01); G06V 10/267 (2022.01); G06V 10/763 (2022.01); G06V 10/774 (2022.01); G06V 20/70 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of adaptive learning for semantic segmentation of images, the method comprising:
obtaining a semantic segmentation model, the semantic segmentation model having been trained using a first subset of sliding windows and corresponding first ground truth masks for the first subset of sliding windows;
ranking a plurality of sliding windows from a corpus of training images according to an uncertainty metric;
selecting a next subset of sliding windows from the corpus of training images based on the ranking and based on a similarity metric for one or more characteristics of a sliding window relative to other sliding windows;
providing a collaborative user interface for labeling the next subset of sliding windows;
receiving ground truth masks for the next subset of sliding windows using the collaborative user interface; and
retraining the semantic segmentation model using the next subset of sliding windows and the ground truth masks for the next subset of sliding windows.