US 12,462,538 B2
Source-free cross domain detection method with strong data augmentation and self-trained mean teacher modeling
Kai Li, Princeton, NJ (US); Renqiang Min, Princeton, NJ (US); and Hans Peter Graf, South Amboy, NJ (US)
Assigned to NEC Corporation, Tokyo (JP)
Filed by NEC Laboratories America, Inc., Princeton, NJ (US)
Filed on Oct. 14, 2022, as Appl. No. 17/966,017.
Claims priority of provisional application 63/279,307, filed on Nov. 15, 2021.
Prior Publication US 2023/0154167 A1, May 18, 2023
Int. Cl. G06V 10/774 (2022.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01)
CPC G06V 10/7747 (2022.01) [G06V 10/25 (2022.01); G06V 10/765 (2022.01); G06V 2201/07 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for implementing source-free domain adaptive detection, the method comprising:
in a pretraining phase:
applying a first level of data augmentation to labeled source images to produce perturbed labeled source images; and
training an object detection model by using the perturbed labeled source images to generate a source-only model; and
in an adaptation phase, train a self-trained mean teacher model by:
generating an augmented image with a second level of data augmentation that is less perturbed than the first level of data augmentation and multiple first level of data augmentation augmented images from unlabeled target images;
generating a plurality of region proposals from the second level augmented image;
selecting a region proposal from the plurality of region proposals as a pseudo ground truth;
detecting, by the self-trained mean teacher model, object boxes and selecting pseudo ground truth boxes by employing a confidence constraint and a consistency constraint; and
training a student model by using one of the multiple first level of data augmentation augmented images jointly with an object detection loss.