US 12,437,516 B2
Method and system for adaptation of a trained object detection model to account for domain shift
Rindranirina Ramamonjison, Burnaby (CA); Amin Banitalebi Dehkordi, Vancouver (CA); Xinyu Kang, Vancouver (CA); and Yong Zhang, Vancouver (CA)
Assigned to HUAWEI CLOUD COMPUTING TECHNOLOGIES CO., LTD., Guiyang (CN)
Filed by Rindranirina Ramamonjison, Burnaby (CA); Amin Banitalebi Dehkordi, Vancouver (CA); Xinyu Kang, Vancouver (CA); and Yong Zhang, Vancouver (CA)
Filed on May 15, 2023, as Appl. No. 18/317,889.
Application 18/317,889 is a continuation of application No. PCT/CA2021/051625, filed on Nov. 16, 2021.
Claims priority of provisional application 63/114,367, filed on Nov. 16, 2020.
Prior Publication US 2023/0281974 A1, Sep. 7, 2023
Int. Cl. G06V 10/774 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/774 (2022.01) [G06V 10/764 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer implemented method for adapting a machine learning model that is configured by a learned set of configuration parameters, comprising:
receiving a plurality of labeled data elements that each include: (i) a source image sample including a respective source data object, and (ii) a corresponding source class label for the respective source data object;
receiving a plurality of target image samples each including a respective target data object;
performing a plurality of model adaptation epochs, wherein for an initial model adaptation epoch the learned set of configuration parameters is used as a current set of configuration parameters for the machine learning model, each model adaptation epoch comprising:
predicting for each of the plurality of targets image samples, using the machine learning model configured by the current set of configuration parameters, a corresponding target class label for the respective target data object included in the target image sample;
generating a plurality of labeled mixed data elements that each include: (i) a mixed image sample including a source data object from one of the source image samples and a target data object from one of the target image samples, and (ii) the corresponding source class label for the source data object and the corresponding target class label for the target data object; and
adjusting the current set of configuration parameters to minimize a loss function for the machine learning model for the plurality of labeled mixed data elements;
and
when performing the plurality of model adaptation epochs is completed, outputting the current set of configuration parameters as a final set of adapted configuration parameters for the machine learning model.