公开(公告)号：US11875526B1

公开(公告)日：2024-01-16

申请号：US18241631

申请日：2023-09-01

Applicant: Deeping Source Inc.

Inventor： Minyong Cho ,  Federica Spinola

IPC: G06T7/70 ,  G06V10/25 ,  G06V10/764 ,  G06V10/762

CPC classification number: G06T7/70 ,  G06V10/25 ,  G06V10/762 ,  G06V10/764 ,  G06T2207/20081 ,  G06V2201/07

Abstract: Method of training an object detector for predicting centers of mass of objects projected onto a ground is provided. The method includes steps of: acquiring training images from training data set; inputting each of training images into the object detector to thereby instruct the object detector to perform object detection for the training images and thus generate object detection results including (i) information on predicted bounding boxes, corresponding to one or more ROIs, acquired by predicting each of locations of the objects in the training images and (ii) information on predicted projection points acquired by projecting the centers of mass of the objects onto the ground; and training the object detector by using object detection losses generated by referring to the object detection results and information on ground truths corresponding to the training images.

公开(公告)号：US20240419959A1

公开(公告)日：2024-12-19

申请号：US18209287

申请日：2023-06-13

Applicant: Deeping Source Inc.

Inventor： Federica Spinola

IPC: G06N3/08

Abstract: There is provided a method for training a multi-tasking network performing multi-tasks by using datasets having different task labels. In response to acquiring specific training data from main dataset including 1-st sub dataset having 1-st task label to n-th sub dataset having n-th task label, a learning device inputs the specific training data into a 1-st multi-tasking network to an n-th multi-tasking network, to thereby instruct the 1-st multi-tasking network to the n-th multi-tasking network to perform learning operation on the specific training data and to output n task results; calculates a 1-st task loss to an n-th task loss by referring to 1-st specific task result to n-th specific task result; calculates a 1-st unlabeled consistency loss group to an n-th unlabeled consistency loss group; and trains the 1-st multi-tasking network to the n-th multi-tasking network by using a total task loss and a total consistency loss.