Abstract: The learning device includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from attributed image data, for each combination of different attributes, using the attributed image data to which attribute information is assigned. The case example storage unit computes the feature vector from the image data for case example to store the computed feature vector as a case example associated with the metric space, and stores additional information associated with the case example.

Abstract: An information processing apparatus (10) according to the present disclosure includes: an object recognition unit (11) that outputs, by using a first modal signal and a first modal recognition model corresponding to the first modal signal, an inference result regarding the first modal signal; a training data processing unit (12) that generates first modal training data regarding the first modal signal by using the inference result, and updates second modal training data regarding a second modal signal that is different from the first modal signal by using the first modal training data; and a recognition model update unit (13) that updates a second modal recognition model corresponding to the second modal signal by using the second modal training data.

Abstract: This image processing device comprises an input unit, a verification area extraction unit, and an output unit. The input unit receives, as an annotation area, an input of information of an area on a first image in which an object subjected to annotation processing is present. The verification area extraction unit extracts a second image including the annotation area and captured in a manner different from that for the first image. The output unit outputs the first image and the second image in a comparable state.

Abstract: An image processing device 100 of the present invention includes a region extraction unit 121 that extracts, from a captured image, a mobile body region that is a region having a possibility that a mobile body exists, a matching unit 122 that performs template matching on the mobile body region by using a template corresponding to a mobile body that is a detection object, and a detection unit 123 that detects each mobile body from the mobile body region separately, on the basis of the template matching.

Abstract: An inspection system includes: a suspended matter detecting and tracking means that detects and tracks suspended matter present in the liquid in the container in chronological images obtained by consecutively imaging the liquid with a camera; a determining and storage controlling means that determines, based on a movement locus of the tracked suspended matter, whether the suspended matter is foreign matter or an air bubble, and stores inspection result information including a result of the determination and information of the movement locus of the suspended matter used as a basis for the determination into a storing means in association with identification information of the container; and a display controlling means that causes a displaying means to display the result of the determination and the information of the movement locus of the suspended matter used as the basis for the determination included by the inspection result information.

Abstract: A determination apparatus includes: a dividing unit configured to divide chronological image data acquired by imaging a liquid filled in a container while switching between a plurality of illumination conditions, into chronological image data corresponding to the illumination conditions; and a determining unit configured to determine foreign matter contained in the container based on each of the chronological image data obtained by division by the dividing unit.

Abstract: An image processing device 100 of the present invention includes an image generation means 121 for generating a difference image representing a difference between an object image that is an image including an area from which a mobile body is to be detected and a corresponding image that is another image including an area corresponding to the area of the object image, and a detection means 122 for detecting the mobile body from the object image on the basis of the object image, the corresponding image, and the difference image.

Abstract: An image processing apparatus according to the present invention includes: an extracting unit configured to extract a candidate image, which is an image of a candidate region specified in accordance with a preset criterion, from a target image to be a target for an annotation process, and also extract a corresponding image, which is an image of a corresponding region corresponding to the candidate region, from a reference image that is an image corresponding to the target image; a displaying unit configured to display the candidate image and the corresponding image so as to be able to compare the images with each other; and an input accepting unit configured to accept input of input information for the annotation process for the candidate image.

Abstract: A prediction unit classifies input data into a plurality of classes using a predictive model, and outputs a predicted probability for each class as a prediction result. A grouping unit generates a grouped class formed by k classes within top k predicted probabilities, and calculates a predicted probability of the grouped class. A loss calculation unit calculates a loss based on predicted probabilities of a plurality of classes including the grouped class. A model update unit updates the predictive model based on the calculated loss.

Abstract: A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.

Abstract: Please delete the Abstract of the Disclosure, and replace it with the following: An input image acquisition unit acquires a plurality of input images in which a specific detection target is captured by a plurality of different modalities. A perturbed image acquisition unit acquires a plurality of perturbed images in which at least one of the plurality of input images is perturbed. A detection processing unit detects a detection target included in the input images using each of the plurality of perturbed images and one of the plurality of input images that has not been perturbed, and acquires, for each of the plurality of perturbed images, a detection position of the detection target and a detection confidence level as detection results. An adjustment unit calculates, based on the detection positions and the confidence levels acquired for the plurality of perturbed images, an adjusted confidence level for each of the perturbed images using integrated parameters.

Abstract: An image generation means 81 generates an image using a generator. A discrimination means 82 discriminates whether an object image includes a feature of a target image, using a discriminator. A first update means 83 updates the generator so as to minimize a first error representing a degree of divergence between a result of discriminating a generated image using the discriminator and a correct answer label associated with the generated image, the generated image being the image generated using the generator. A second update means 84 updates the discriminator so as to minimize a second error representing a degree of divergence between each of respective results of discriminating the generated image, a first actual image including the feature of the target image, and a second actual image not including the feature of the target image using the discriminator and a correct answer label associated with a corresponding image.

Abstract: An information processing apparatus (10) according to the present disclosure includes: an object recognition unit (11) that outputs, by using a first modal signal and a first modal recognition model corresponding to the first modal signal, an inference result regarding the first modal signal; a training data processing unit (12) that generates first modal training data regarding the first modal signal by using the inference result, and updates second modal training data regarding a second modal signal that is different from the first modal signal by using the first modal training data; and a recognition model update unit (13) that updates a second modal recognition model corresponding to the second modal signal by using the second modal training data.

Abstract: The model generation device generates model parameters corresponding to the model to be used and mediation parameter relevance information indicating the relevance between the model parameters of a plurality of source domains and the mediation parameters by using the learning data in the plurality of source domains. The model adjustment device generates target model parameters which correspond to the target domain and include the mediation parameters, based on the learned model parameters for each of the plurality of source domains and the mediation parameter relevance information. Then, the model adjustment device uses the evaluation data of the target domain to determine the mediation parameters included in the target model parameters.

Abstract: The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit (401) transforms a plurality of images acquired by an image acquisition unit (407). A reliability level calculation unit (402) calculates a level of reliability representing how small the misalignment between images is. A score calculation unit (405) calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (404), and the level of reliability calculated by the reliability level calculation unit (402). An object detection unit (406) detects an object in the images on the basis of the total score.

Abstract: A data-series group includes data series which is a series of data obtained by observing the same object at discrete times. Time labels are time information added to respective data included in the data-series group. State labels are added to some of the data included in the data-series group. A loss-function control unit determines a loss function to be used for learning based on the time labels and the state labels. A threshold is used to adjust a branch condition of the loss-function control unit. A regressor is a model, and is used to detect an abnormality or predict a remaining life span. A dictionary stores parameters of the regressor. A regressor training unit trains the regressor based on the loss function determined by the loss-function control unit.

Abstract: The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit (401) transforms a plurality of images acquired by an image acquisition unit (407). A reliability level calculation unit (402) calculates a level of reliability representing how small the misalignment between images is. A score calculation unit (405) calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (404), and the level of reliability calculated by the reliability level calculation unit (402). An object detection unit (406) detects an object in the images on the basis of the total score.

Abstract: Disclosed is a model generation device capable of mitigating the risk of overlooking a phenomenon of interest in machine learning. The model generation device determines whether or not a label of a first data is similar to a label of a second data. The model generation device assigns the label of the second data to the first data when determining that the label of the first data is similar to the label of the second data based on a degree of similarity between observation information representing a state where the first data is observed and observation information representing a state where the second data is observed. The model generation device calculates model representing a relevance between data information containing the first data and the second data and label information containing the assigned label and the label of the second data.

Abstract: An image processing apparatus (1) includes: a determination unit (11) configured to determine, using a ground truth, a degree to which each of a plurality of candidate areas that correspond to respective predetermined positions common to the plurality of images includes a corresponding ground truth area for each of the plurality of images; and a first learning unit (12) configured to learn, based on a plurality of feature maps extracted from each of the plurality of images, a set of the results of the determination made by the determination means, and the ground truth, a parameter (14) used when an amount of positional deviation between the position of the detection target included in a first image captured by a first modal and the position of the detection target included in a second image captured by a second modal is predicted.

Abstract: An image generation means 81 generates an image using a generator. A discrimination means 82 discriminates whether an object image includes a feature of a target image, using a discriminator. A first update means 83 updates the generator so as to minimize a first error representing a degree of divergence between a result of discriminating a generated image using the discriminator and a correct answer label associated with the generated image, the generated image being the image generated using the generator. A second update means 84 updates the discriminator so as to minimize a second error representing a degree of divergence between each of respective results of discriminating the generated image, a first actual image including the feature of the target image, and a second actual image not including the feature of the target image using the discriminator and a correct answer label associated with a corresponding image.