US 12,423,812 B2
Information processing apparatus, information processing method, and non-transitory computer-readable storage medium for training an estimation model such that a loss is reduced
Saeko Sasuga, Tokyo (JP)
Assigned to FUJIFILM Corporation, Tokyo (JP)
Filed by FUJIFILM Corporation, Tokyo (JP)
Filed on Mar. 21, 2023, as Appl. No. 18/186,954.
Application 18/186,954 is a continuation of application No. PCT/JP2021/027590, filed on Jul. 26, 2021.
Claims priority of application No. 2020-163872 (JP), filed on Sep. 29, 2020.
Prior Publication US 2023/0230247 A1, Jul. 20, 2023
Int. Cl. G06T 7/00 (2017.01); G06T 7/12 (2017.01); G06T 7/62 (2017.01); G06V 10/25 (2022.01); G06V 10/74 (2022.01); G06V 10/75 (2022.01)
CPC G06T 7/12 (2017.01) [G06T 7/62 (2017.01); G06V 10/25 (2022.01); G06V 10/758 (2022.01); G06V 10/761 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/30096 (2013.01)] 15 Claims
OG exemplary drawing
 
1. An information processing apparatus comprising:
at least one processor,
wherein the processor
acquires a plurality of first training data in which area information indicating an area in which each of a plurality of regions is present is added to a first training image which is at least a part of a plurality of training images each including the plurality of regions, and a plurality of second training data in which relationship information indicating a relationship between the plurality of regions is added to a second training image which is at least a part of the plurality of training images,
calculates, for each first training image, a first evaluation value for training an estimation model such that the plurality of regions specified by using the estimation model match the area information,
derives, for each second training image, estimation information in which the relationship indicated by the relationship information is estimated by using the estimation model to calculate a second evaluation value indicating a degree of deviation between the estimation information and the relationship information, and
trains the estimation model such that a loss including, as elements, the first evaluation value and the second evaluation value is reduced.