Abstract: An information processing apparatus includes an acquisition section that acquires at least two first evaluation values obtained by evaluating a motion of a worker in time series and a reference evaluation value obtained by evaluating a motion of an expert as an exemplar of the worker, a calculation section that calculates at least two motion levels indicating similarity between the at least two first evaluation values and the reference evaluation value, and an output section that outputs a development degree indicating a degree of development of the worker based on the at least two motion levels.

Abstract: An information processing apparatus includes a receiving unit that receives an input picture obtained by capturing a landscape including an object, and an estimation unit that estimates an image represented by the landscape appearing in the input picture based on a learning model in which the input picture received by the receiving unit is input.

Abstract: An information processing apparatus includes a receiving unit that receives a first image which has been extracted from an image obtained by imaging of an omnidirectional camera, the first image having been obtained by imaging a head of a person and position information indicating a position of the person in the image, a calculation unit that calculates a degree of distortion of the first image from the position information, and a learning unit that performs learning for estimating an orientation of a face of the person by using the calculated degree of distortion and the first image.

Abstract: A person identification device includes: an extractor that, from an image containing a person, extracts a first characteristic quantity related to a face of the person and a second characteristic quantity related to a body of the person; and an identifying unit that identifies the person based on a third characteristic quantity calculated by assigning a weight to each of the first characteristic quantity and the second characteristic quantity extracted.

Abstract: An information processing apparatus includes an acquisition unit that acquires first impression information representing a first impression and second impression information representing a second impression for each of plural images including an image in which a subject is imaged and plural partial images including a part of the subject, the first impression being an impression received by a person, and the second impression being an impression received by the person and different from the first impression, a setting unit that sets a weight corresponding to the corresponding second impression information for the first impression information related to each of the plural images based on each of the plural images and the second impression information, and an output unit that outputs the first impression of the image in which the subject is imaged from the first impression information related to each of the plural images using the weight set by the setting unit.

Abstract: An information processing apparatus includes a receiving unit that receives an input picture obtained by capturing a landscape including an object, and an estimation unit that estimates an image represented by the landscape appearing in the input picture based on a learning model in which the input picture received by the receiving unit is input.

Abstract: An image pattern recognition device includes: a data reception unit that receives data; a supervision reception unit that receives supervision; and an artificial neural network processing unit that performs artificial neural network processing. The artificial neural network processing unit includes a first sub-network including one or more layers that process a main task, a second sub-network including one or more layers that process a sub-task, and a third sub-network including one or more layers that do not belong to any of the first sub-network and the second sub-network. The third sub-network includes a branch processing unit that outputs a value same as an input feature amount to a plurality of layers. The first sub-network includes a coupling processing unit that couples inputs from the plurality of layers and outputs a result.

Abstract: A data classification device includes an estimation unit that estimates, for each of one or more classes provided for learning data pieces in a feature-amount-data space that includes multiple learning data pieces, probability densities of learning data pieces belonging to the class and learning data pieces not belonging to the class around a judgment target data piece in the feature-amount-data space, a calculation unit that calculates, based on the probability densities, an index indicating how much the judgment target data piece is likely to belong to the class, and a judgment unit that judges which class the judgment target data piece belongs to by using the index. Based on distribution of positive data pieces belonging to the class and negative data pieces not belonging to the class around the judgment target data piece, the estimation unit determines a size of a region used for the estimation.

Abstract: An information processing apparatus includes a receiving unit that receives a first image which has been extracted from an image obtained by imaging of an omnidirectional camera, the first image having been obtained by imaging a head of a person and position information indicating a position of the person in the image, a calculation unit that calculates a degree of distortion of the first image from the position information, and a learning unit that performs learning for estimating an orientation of a face of the person by using the calculated degree of distortion and the first image.

Abstract: An information processing apparatus includes an acquisition section that acquires at least two first evaluation values obtained by evaluating a motion of a worker in time series and a reference evaluation value obtained by evaluating a motion of an expert as an exemplar of the worker, a calculation section that calculates at least two motion levels indicating similarity between the at least two first evaluation values and the reference evaluation value, and an output section that outputs a development degree indicating a degree of development of the worker based on the at least two motion levels.

Abstract: There is provided a class estimation apparatus. A data reception unit receives an input of a feature amount of data. An identification device reception unit receives an input of an identification device having a tree structure. A table storage unit stores a probability table and a feature amount table in a leaf node of the tree structure. A probability table correction unit corrects the probability table. A class estimation unit estimates a class of the data.

Abstract: A person identification device includes: an extractor that, from an image containing a person, extracts a first characteristic quantity related to a face of the person and a second characteristic quantity related to a body of the person; and an identifying unit that identifies the person based on a third characteristic quantity calculated by assigning a weight to each of the first characteristic quantity and the second characteristic quantity extracted.

Abstract: A method of generating a predictive model of categories of venues visited by a user is provided. The method may include extracting a first content feature from a first digital post to an online social media platform selected from a plurality of digital posts, extracting a second content feature from a second digital post to an online social media platform selected from the plurality of social media posts, aggregating the first and second content features, inferring at least one of a frequency and a regularity of visits to a venue category associated with the plurality of digital posts based on the aggregated first and second content features using a neural network, and determining at least one of a frequently visited venue category and a regularly visited venue category based on the inferred frequency and a regularity of visits associated with the plurality of digital posts.

Abstract: An image processing apparatus includes a reception section, an image extraction section, a forming section, and a comparison section. The reception section receives a video. The image extraction section extracts target object images from multiple frames that constitute the video received by the reception section. The forming section forms multiple target object images among the target object images extracted by the image extraction section into one unit, the multiple target object images being temporally apart from each other. The comparison section makes a comparison on the basis of the unit formed by the forming section.

Abstract: There is provided a class estimation apparatus. A data reception unit receives an input of a feature amount of data. An identification device reception unit receives an input of an identification device having a tree structure. A table storage unit stores a probability table and a feature amount table in a leaf node of the tree structure. A probability table correction unit corrects the probability table. A class estimation unit estimates a class of the data.

Abstract: An image pattern recognition device includes: a data reception unit that receives data; a supervision reception unit that receives supervision; and an artificial neural network processing unit that performs artificial neural network processing. The artificial neural network processing unit includes a first sub-network including one or more layers that process a main task, a second sub-network including one or more layers that process a sub-task, and a third sub-network including one or more layers that do not belong to any of the first sub-network and the second sub-network. The third sub-network includes a branch processing unit that outputs a value same as an input feature amount to a plurality of layers. The first sub-network includes a coupling processing unit that couples inputs from the plurality of layers and outputs a result.

Abstract: A data classification device includes an estimation unit that estimates, for each of one or more classes provided for learning data pieces in a feature-amount-data space that includes multiple learning data pieces, probability densities of learning data pieces belonging to the class and learning data pieces not belonging to the class around a judgment target data piece in the feature-amount-data space, a calculation unit that calculates, based on the probability densities, an index indicating how much the judgment target data piece is likely to belong to the class, and a judgment unit that judges which class the judgment target data piece belongs to by using the index. Based on distribution of positive data pieces belonging to the class and negative data pieces not belonging to the class around the judgment target data piece, the estimation unit determines a size of a region used for the estimation.

Abstract: There is provided a non-transitory computer readable medium storing a program causing a computer to execute a process for attribute estimation. The process includes: extracting, for each user, feature quantities of plural pieces of image information that are associated with attributes of the user; integrating the extracted feature quantities for each user; and performing learning, input of the learning being an integrated feature quantity that has been obtained as a result of integration for each user, output of the learning being one attribute, and generating a learning model.

Abstract: There is provided a non-transitory computer readable medium storing a program causing a computer to execute a process for attribute estimation. The process includes: extracting, for each user, feature quantities of plural pieces of image information that are associated with attributes of the user; integrating the extracted feature quantities for each user; and performing learning, input of the learning being an integrated feature quantity that has been obtained as a result of integration for each user, output of the learning being one attribute, and generating a learning model.

Abstract: A non-transitory computer readable medium stores an annotation-information adding program that causes a computer to function as an adding unit, an evaluating unit, and a setting unit. The adding unit adds annotation information to target information including multiple targets based on input from a first inputter. The evaluating unit evaluates reliability of the first inputter and reliability of a second inputter by comparing annotation information already added to at least one of the multiple targets by the second inputter with annotation information added by the first inputter. The setting unit sets a target range in the target information intended for requesting the first inputter to add annotation information based on the reliability of the first inputter and the reliability of the second inputter.