Abstract:
An imaging control device includes a correct answer information acquiring part that acquires first correct answer information corresponding to a training image of a machine learning model from a first storage part, a correct answer image display control part that causes a display device to display a first correct answer image based on the first correct answer information, an imaging control part that causes an imaging device that acquires a blurred image to capture the first correct answer image displayed on the display device and acquires a second correct answer image, a correct answer information generating part that generates second correct answer information based on the second correct answer image, and a storage control part that stores a data set including a set of the training image and the second correct answer information in a second storage part.
Abstract:
An information processing method includes obtaining a first inference result by a first inference model, obtaining a second inference result by a second inference model, and training the second inference model by machine learning to reduce an error calculated from the first inference result and the second inference result. The second inference model includes (a) a first coefficient pertaining to a domain and used for inference by the second inference model and (b) a second coefficient used for inference by the second inference model. The training includes; when it is determined that a predetermined condition is not satisfied, training the second inference model with the first coefficient and the second coefficient designated as targets for an update; and when it is determined that the predetermined condition is satisfied, training the second inference model with the second coefficient designated as a target for an update.
Abstract:
An information processing method includes processing of: acquiring, from a plurality of time-series images in which an object is captured, first information including at least a plurality of positions or a plurality of sizes of the object; executing prediction processing of predicting second information including at least one of a position or a size of the object at a next time point in a time-series based on the first information and recursively executing the prediction processing based on the first information and the second information to predict the second information of the object at a time point further next to the next time point; executing recognition processing of recognizing motion of the object based on the second information; and determining a total number of times of recursion of the prediction processing based on a result of the recognition processing.
Abstract:
An information processing device is an information processing device including a processor. The processor obtains a detection result of a first detector for detecting a first target in first sensing data; and based on the detection result of the first detector, determines a setting of processing by a second detector for detecting a second target in second sensing data next in an order after the first sensing data, the second target being different from the first target.
Abstract:
An information processing method includes acquiring first output data for input data of first learning model, reference data for the input data, and second output data for the input data of second learning model obtained by converting first learning model; calculating first difference data corresponding to a difference between the first difference data and the reference data and second difference data corresponding to a difference between the second output data and the reference data; and training first learning model with use of the first difference data and the second difference data.
Abstract:
A navigation device (100) estimates a time when a user utilizing the navigation device (100) comes home, and transmits the estimated expected arrival time to a control device (203) in association with the user identifier of the user. In response to receiving the expected arrival times from a plurality of the navigation devices (100), the control device (203) transmits, to an air conditioner (201), a reservation instruction for setting the room temperature at the home to a predetermined target temperature by the estimated arrival time of the earliest user who is supposed to come home earliest. The air conditioner (201) is turned on in accordance with the transmitted reservation instruction among the users.
Abstract:
A training data generation method for generating training data for training a recognition model that is input with image data and outputs one of a plurality of classes as a class of an object present in the image data, the training data generation method including: selecting a first class from the plurality of classes based on a recognition accuracy of the recognition model; calculating an inter-class distance that is a distance between the first class and each of two or more other classes among the plurality of classes; selecting a second class for generating the training data from the two or more other classes, based on the inter-class distance; and generating the training data by mixing image data and labels of each of the first class selected and the second class selected.
Abstract:
A training method includes: obtaining an image and a distance image corresponding to the image; cutting a partial area out from the distance image obtained; generating an embedded image by pasting the partial area cut out from the distance image onto a predetermined area in the image, where the predetermined area is located at a position corresponding to the position of the partial area and has a size corresponding to the size of the partial area; and training a machine learning model, using training data including the embedded image as input data and the distance image as correct answer data.
Abstract:
An information processing method includes: obtaining a first inference model serving as a reference; computing a second inference model that is larger than the first inference model in model size, based on the first inference model; quantizing the second inference model computed to generate a third inference model; training the third inference model, using machine learning; determining whether a performance of the third inference model trained satisfies a condition; and outputting the third inference model trained, when the performance satisfies the condition.
Abstract:
An interval between a pinhole and a pinhole is set to a first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of a subject located at a distance less than a predetermined distance from the multi-pinhole camera is captured. An interval between the pinhole and a pinhole is set to a second interval narrower than the first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of the subject located at a distance equal to or more than the predetermined distance from the multi-pinhole camera is captured.