| CPC G06T 7/0012 (2013.01) [G06V 10/82 (2022.01); G16H 50/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01)] | 14 Claims |
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1. A learning device comprising:
at least one processor,
wherein the processor is configured to:
derive a first feature amount for an object included in an image by a first neural network,
derive a second feature amount for a sentence including description of an object by a second neural network,
acquire each of a first attribute, which is an attribute of the object included in the image, and a second attribute, which is an attribute of the sentence, and
construct a first derivation model that derives a feature amount for the object included in the image and a second derivation model that derives a feature amount for the sentence including the description of the object by training the first neural network and the second neural network, wherein, in response to training the first neural network and the second neural network, in a feature space to which the first feature amount and the second feature amount belong, the first attribute and the second attribute are determined as being more relevant as a distance in the feature space between the derived first feature amount and the derived second feature amount is reduced, and the first attribute and the second attribute are determined as being less relevant as the distance in the feature space between the derived first feature amount and the derived second feature amount is increased.
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