Abstract: In the object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists, for each partial region set with respect to image data inputted. The weight computation unit computes a weight for each of the plurality of object detection units by using weight computation parameters and based on the image data. The weights are used when the scores outputted by the plurality of object detection units are merged. The weight redistribution unit changes the weight for a predetermined object detection unit, among the weights computed by the weight computation unit, to 0 and output the weights. The merging unit merges the scores outputted by the plurality of object detection units for each of the partial regions, by using the weights computed by the weight computation unit and including the weight changed by the weight redistribution unit.

Abstract: A learning-model generation apparatus 10 includes: an all-feature-amount-outputting unit that output, from image data of an object and for each joint of the object, a feature amount representing the joint; a feature-amount-generating unit that generates, from the feature amounts of the individual joints of the object and as training feature amounts, feature amounts in a case in which the feature amount of a certain joint is missing; and a learning-model-generating unit that, by using training data including the generated training feature amounts, generates a machine learning model by machine-learning positional relationships between the other joints in the case in which the feature amount of the certain joint is missing.

Abstract: A computer-implemented method can comprise accessing a trained learning machine, evaluating, using the machine learning model, the transcript to output a first sentiment score related to the first party in the unique domain, accessing digital engagement data representing engagement of the first party with digital assets associated with the second party, evaluating the one or more sentiment score values and the digital engagement data to output a value indicative of a likelihood of the first party to take a particular action, and determining whether the value is above a threshold, and if so, automatically sending a notification to a computer device associated with the second party.

Abstract: In an object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists for each partial region set with respect to inputted image data. The weight computation unit uses weight computation parameters to compute a weight for each of the plurality of object detection units on a basis of the image data and outputs of the plurality of object detection units, the weight being used when the scores outputted by the plurality of object detection units are merged. The merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. The first loss computation unit computes a difference between a ground truth label of the image data and the score merged by the merging unit as a first loss. Then, the first parameter correction unit corrects the weight computation parameters so as to reduce the first loss.

Abstract: A vibration measurement apparatus 30 includes a detection unit 31 that acquires, as a pattern image from an image capturing apparatus 20 that shoots a measurement target surface of a structure 40, an image of the measurement target surface onto which pattern light is projected by an optical apparatus 10, and detects the pattern light from the pattern image, an estimation unit 32 that estimates an angle between the normal of the image capturing surface and the normal of the measurement target surface, based on the pattern light, an image conversion unit 33 that converts the shot image into an image that would be obtained were the normal of the measurement target surface coincident with the normal of the image capturing surface of the image capturing apparatus 20, using the estimated angle, and a vibration measurement unit 34 that measures the vibration of the structure 40 using the converted image.

Abstract: A computer-implemented method can comprise accessing a trained learning machine, evaluating, using the machine learning model, the transcript to output a first sentiment score related to the first party in the unique domain, accessing digital engagement data representing engagement of the first party with digital assets associated with the second party, evaluating the one or more sentiment score values and the digital engagement data to output a value indicative of a likelihood of the first party to take a particular action, and determining whether the value is above a threshold, and if so, automatically sending a notification to a computer device associated with the second party.

Abstract: The information processing device performs distillation learning of a student model using unknown data which a teacher model has not learned. The label distribution determination unit outputs an arbitrary label for the unknown data. The data generation unit outputs new generated data using an arbitrary label and unknown data as inputs. The distillation learning part performs distillation learning of the student model using the teacher model and using the generated data as an input.

Abstract: Teacher and student models output inference results for training data. A loss calculation unit calculates a total loss using at least one of (1) a loss obtained by multiplying a difference between a true value and a student model output by a weight increasing as a confidence of the teacher model output is lower, (2) a loss obtained by multiplying a difference between the true value and the student model output by a weight increasing as a difference between the true value and the teacher model output is greater, and (3) a loss obtained by multiplying a difference between the teacher and student model outputs by weights increasing as the difference between the teacher and student model outputs is greater and increasing as the difference between the true value and the teacher model output is smaller. An update part updates parameters of the student model based on the total loss.

Abstract: The server device receives a model information from a plurality of terminal devices, and generates an integrated model by integrating the model information received from the plurality of terminal devices. The server device generates an updated model by learning a model defined by the model information received from the terminal device of update-target using the integrated model. Then, the server device transmits the model information of the updated model to the terminal device. Thereafter, the terminal device executes recognition processing using updated model.

Abstract: In an object detection device, a plurality of object detection units output a score indicating the probability that a predetermined object exists for each partial region set with respect to inputted image data. On the basis of the image data, a weight computation unit uses weight computation parameters to compute weights for each of the plurality of object detection units, the weights being used when the scores outputted by the plurality of object detection units are merged. A merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. A loss computation unit computes a difference between a ground truth label of the image data and the scores merged by the merging unit as a loss. Then, a parameter correction unit corrects the weight computation parameters so as to reduce the computed loss.

Abstract: In an object detection device, a plurality of object detection units output a score indicating probability that a predetermined object exists, for each partial region set to image data inputted. The weight computation unit computes weights for merging the scores outputted by the plurality of object detection units, using weight calculation parameters, based on the image data. The merging unit merges the scores outputted by the plurality of object detection units, for each partial region, with the weights computed by the weight computation unit. The target model object detection unit configured to output a score indicating probability that the predetermined object exists, for each partial region set to the image data. The first loss computation unit computes a first loss indicating a difference of the score of the target model object detection unit from a ground truth label of the image data and the score merged by the merging unit.

Abstract: The learning device includes: a first estimation unit configured to perform estimation using a temperature parameter, based on a student model; a second estimation unit configured to perform estimation using the temperature parameter, based on a teacher model; and a temperature calculation unit configured to calculate the temperature parameter, based on estimation information generated by the first estimation unit and the second estimation unit.

Abstract: An image processing apparatus includes a dividing unit, a measuring unit, a comparing unit, and a determining unit. The dividing unit spatially divides a time-series image of a structure surface captured during passage of a traffic load into a plurality of partial regions, and generates a plurality of partial time-series images. The measuring unit measures temporal changes in deflection amount of the structure surface in the respective partial regions from the plurality of partial time-series images. The comparing unit compares the temporal changes in deflection amount of the structure surface in the respective partial regions. The determining unit determines an orientation of the time-series image with respect to a passage direction of the traffic load based on a result of the comparison.

Abstract: An apparatus includes: a deflection acquiring unit that acquires a deflection amount caused in a measurement target region on a structure by a vehicle traveling on the structure; a vehicle position acquiring unit that acquires a position of the vehicle on the structure at time when the deflection amount is acquired; and a rigidity coefficient calculating unit that calculates a rigidity coefficient specifying a relation between a magnitude of a force applied to the structure due to a weight of the vehicle and flexural rigidity of the structure, from a relation equation, the acquired deflection amount, and the detected position of the vehicle. The relation equation is established among a position of the vehicle on the structure, the magnitude of the force, a position of the measurement target region on the structure, a length of the structure, the flexural rigidity, and a deflection amount caused in the measurement target region.

Abstract: A vibration measurement system comprises an image capturing apparatus, a distance measuring apparatus, a sensor that outputs a signal according to an inclination of the image capturing apparatus relative to the vertical direction, and a vibration measurement apparatus. The vibration measurement apparatus includes calculating an angle formed by the normal of an image capturing surface of the image capturing apparatus and the normal of the measurement target surface that the image capturing apparatus shoots, based on the signal output by the sensor, converting the image obtained that the image capturing apparatus shoots into an image that would be obtained were the normal of the measurement target surface coincident with the normal of the image capturing surface of the image capturing apparatus, using the calculated angle, and measuring vibration of the structure, using the converted image and the measured distance from the image capturing apparatus to the measurement target surface.

Abstract: An information processing apparatus (2000) acquires observation information including a result of observing a structure (10) to which a moving load is applied, and determines, by using the observation information and a soundness condition, whether a relation between a temporal change in deflection amount of the structure (10) and a temporal change in an application position satisfies the soundness condition. The soundness condition is a condition, for deflection caused in the structure (10) by applying a load to the structure (10) while changing the application position, being satisfied when the structure (10) is sound. Then, the information processing apparatus (2000) outputs, based on a result of the determination, information relating to a degree of soundness of the structure (10).

Abstract: A vibration measurement apparatus 30 includes a detection unit 31 that acquires, as a pattern image from an image capturing apparatus 20 that shoots a measurement target surface of a structure 40, an image of the measurement target surface onto which pattern light is projected by an optical apparatus 10, and detects the pattern light from the pattern image, an estimation unit 32 that estimates an angle between the normal of the image capturing surface and the normal of the measurement target surface, based on the pattern light, an image conversion unit 33 that converts the shot image into an image that would be obtained were the normal of the measurement target surface coincident with the normal of the image capturing surface of the image capturing apparatus 20, using the estimated angle, and a vibration measurement unit 34 that measures the vibration of the structure 40 using the converted image.

Abstract: A vibration measurement system comprises an image capturing apparatus, a distance measuring apparatus, a sensor that outputs a signal according to an inclination of the image capturing apparatus relative to the vertical direction, and a vibration measurement apparatus. The vibration measurement apparatus includes calculating an angle formed by the normal of an image capturing surface of the image capturing apparatus and the normal of the measurement target surface that the image capturing apparatus shoots, based on the signal output by the sensor, converting the image obtained that the image capturing apparatus shoots into an image that would be obtained were the normal of the measurement target surface coincident with the normal of the image capturing surface of the image capturing apparatus, using the calculated angle, and measuring vibration of the structure, using the converted image and the measured distance from the image capturing apparatus to the measurement target surface.