Information processing apparatus, information processing method, and storage medium

The information processing apparatus comprising:

• an acquisition unit configured to acquire a plurality of modalities associated with an object and information identifying the object;
• a feature generation unit configured to generate feature values for each of the plurality of modalities;
• a deriving unit configured to derive weights corresponding to each of the plurality of feature values by applying the feature values for each of the plurality of modalities and the information identifying the object to the second learning model, and
• a prediction unit configured to predict an attribute of the object by applying a concatenated value of the feature values of each for the plurality of modalities, weighted by the corresponding weights, to the third learning model, wherein
• the second learning model is a learning model that outputs different weights for each object.

An information processing method comprising:

• acquiring a plurality of modalities associated with an object and information identifying the object;
• generating feature values for each of the plurality of modalities;
• deriving weights corresponding to each of the plurality of modalities based on the feature values for each of the plurality of modalities and information identifying the object;
• and
• predicting an attribute of the object from a concatenated value of the feature values for each of the plurality of modalities, weighted by the corresponding weights.

An information processing method comprising:

• acquiring a plurality of modalities associated with an object and information identifying the object;
• generating feature values for each of the plurality of modalities;
• deriving weights corresponding to each of the plurality of feature values by applying the feature values for each of the plurality of modalities and the information identifying the object to the second learning model, and
• predicting an attribute of the object by applying a concatenated value of the feature values of each for the plurality of modalities, weighted by the corresponding weights, to the third learning model, wherein
• the second learning model is a learning model that outputs different weights for each object.

A non-transitory computer-readable storage medium storing computer executable instructions for causing a computer to implement an information processing method, the information processing method comprising:

• acquiring a plurality of modalities associated with an object and information identifying the object;
• generating feature values for each of the plurality of modalities;
• deriving weights corresponding to each of the plurality of modalities based on the feature values for each of the plurality of modalities and information identifying the object;
• and
• predicting an attribute of the object from a concatenated value of the feature values for each of the plurality of modalities, weighted by the corresponding weights.

A non-transitory computer-readable storage medium storing computer executable instructions for causing a computer to implement an information processing method, the information processing method comprising:

• acquiring a plurality of modalities associated with an object and information identifying the object;
• generating feature values for each of the plurality of modalities;
• deriving weights corresponding to each of the plurality of feature values by applying the feature values for each of the plurality of modalities and the information identifying the object to the second learning model, and
• predicting an attribute of the object by applying a concatenated value of the feature values of each for the plurality of modalities, weighted by the corresponding weights, to the third learning model, wherein
• the second learning model is a learning model that outputs different weights for each object.