Abstract: A system includes: a unit that input a series image sequence; a unit that selects a reference image; a unit that selects a proximity image; and an inference unit that recognizes the reference image and the proximity image by performing inference processing, including convolution processing and activation function processing, on the reference image and the proximity image. The inference unit generates results of performing the convolution processing and the activation function processing on the proximity image from the results of the convolution processing and the activation function processing performed on the reference image, and the results of the product of the results of the convolution processing performed on a difference image, which is an image of the difference between the reference image and the proximity image, and a derivative value of the results of the convolution processing and the activation function processing performed on the reference image.

Abstract: In an inspection device, a classification means classifies temporal captured images which capture a target object, into a plurality of groups. A recognition means recognizes the captured images belonging to each of the groups, and outputs a determination result for each of the groups. An integration means integrates respective determination results of the groups, and outputs a final determination result.

Abstract: In a learning device, an acquisition means acquires captured images in a time series which capture a target object. Next, a learning means simultaneously trains a group discrimination model for discriminating a plurality of groups from the captured images based on features in each image and a plurality of recognition models each for recognizing captured images belonging to a corresponding group.

Abstract: A data acquisition means acquires source domain data and target domain data. An alignment means performs an alignment which converts the source domain data and the target domain data into images of a predetermined reference angle. A feature extraction means extracts local features of the source domain data and the target domain data. A classification means classifies a class based on the local features of the source domain data and the target domain data after the alignment. A learning means trains the feature extraction means based on the local features of the source domain data and the target domain data after the alignment and a classification result of the class.

Abstract: In order to ensure necessary inference accuracy while minimizing inference time, an information processing apparatus (1) includes: a first difficulty calculation unit (11) that calculates difficulty in inference carried out by inputting input data, constituting a time series, to a first-stage inference model among multiple-stage inference models which are configured such that use of a later-stage inference model achieves higher inference accuracy; and a first determination unit (12) that determines, on the basis of the difficulty, whether a second- or later-stage inference model will be used.

Abstract: A system includes: a sequential image string input unit configured to input a sequential image string having sequentiality; a reference image selection unit configured to select one or more images from the sequential image string as reference images; a variation calculation unit configured to select an adjacent reference image adjacent to the reference image from the sequential image string and calculate a variation between the reference image and the adjacent reference image; an image information regression unit configured to calculate class confidence by regression processing with the reference image as an input; a difference image information regression unit configured to calculate class confidence by regression processing with the variation as an input; a confidence integration unit configured to integrate class confidence calculated by the image information regression unit and class confidence calculated by the difference image information regression unit; and an output unit configured to output the inte

Abstract: In a data augmentation device, a data acquisition means acquires two sets of source domain data of a predetermined class from a data group of a source domain, and acquires one set of target domain data of the predetermined class from a data group of a target domain data. An estimation means estimates a structure of a manifold representing a data distribution of the source domain by using two sets of source domain data. A data generation means generates new data of the target domain by using the one set of target domain data and the structure of the manifold.

Abstract: The image recognition apparatus includes an image selection unit and a recognition unit. The image selection unit selects a feature image representing a feature portion of an object from among captured images of a time series in which the object is photographed. For example, the feature image corresponds to an image showing an abnormal portion. The recognition unit performs a recognition process of the object using the feature image. By the recognition process, an abnormality of the object is detected.

Abstract: A system includes: a sequential image string input unit configured to input a sequential image string having sequentiality; a reference image selection unit configured to select one or more images from the sequential image string as reference images; a variation calculation unit configured to select an adjacent reference image adjacent to the reference image from the sequential image string and calculate a variation between the reference image and the adjacent reference image; an image information regression unit configured to calculate class confidence by regression processing with the reference image as an input; a difference image information regression unit configured to calculate class confidence by regression processing with the variation as an input; a confidence integration unit configured to integrate class confidence calculated by the image information regression unit and class confidence calculated by the difference image information regression unit; and an output unit configured to output the inte

Abstract: The learning apparatus classifies target domain data into (N-c) classes based on unique features of the target domain data, classifies source domain data into N classes based on unique features of the source domain data, and classifies the target domain data and the source domain data into the N classes based on common features of the target domain data and the source domain data. Also, the learning apparatus calculates a first distance between the common features of the target domain data and the source domain data, and calculates a second distance between the unique features of the target domain data and the source domain data. Next, the learning apparatus updates parameters of a common feature extraction unit based on the first distance, and updates parameters of a target domain feature extraction unit and a source domain feature extraction unit based on the second distance.

Abstract: A system includes: a unit that input a series image sequence; a unit that selects a reference image; a unit that selects a proximity image; and an inference unit that recognizes the reference image and the proximity image by performing inference processing, including convolution processing and activation function processing, on the reference image and the proximity image. The inference unit generates results of performing the convolution processing and the activation function processing on the proximity image from the results of the convolution processing and the activation function processing performed on the reference image, and the results of the product of the results of the convolution processing performed on a difference image, which is an image of the difference between the reference image and the proximity image, and a derivative value of the results of the convolution processing and the activation function processing performed on the reference image.