Abstract: A computer vision system, with at least one processor configured to: acquire, from a sports match video, a plurality of pieces of consecutive image data indicating a portion of the sports match video, the plurality of pieces of consecutive image data including a plurality of pieces of first consecutive image data that are consecutive and a plurality of pieces of second consecutive image data that are consecutive after the plurality of pieces of first consecutive image data; and execute an estimation, by using a machine learning model, of whether the portion is of a predetermined scene type.

Abstract: To determine whether product information provided from a seller is consistent, provided is a learning device including at least one processor, the at least one processor being configured to: acquire first and second product information from a storage in which a plurality of pieces of product information each including a plurality of pieces of item data relating to a product are stored in advance; generate third product information in which at least one piece of item data included in the first product information is replaced with the corresponding at least one piece of item data of the second product information; and cause a machine learning model to learn the third product information as inconsistent product information, wherein the machine learning model is configured to determine, when a piece of product information is input, whether the piece of product information is consistent.

Abstract: Provided is an information-processing device for extracting an important image from target information including images and a text, the information-processing device including: a CPU; and a memory storing instructions for causing the information-processing device, when executed by the CPU, to: determine a category indicating an attribute of each image; extract a category for each division of the text; determine a score of the category based on the text; and extract, as a most important image, one of the images corresponding to the category selected based on the score.

Abstract: When being connected to a macro cell (S-MeNB) and a small cell (SeNB), a user equipment device performs a pre-handover process of disconnecting the connection with the SeNB before a handover process of switching a macro cell connected with the UE from the macro cell (S-MeNB) that is a moving source to a macro cell (T-MeNB) that is a moving destination along with moving of the UE, and after the handover process, performs a post-handover process of reestablishing the connection with the SeNB.

Abstract: An information processing apparatus acquires a plurality of images related to a specified article, decides suitability of each of the plurality of images for estimating a state of the article, and outputs state information indicating an estimated state of the specified article based on a result of the decision and at least a part of the plurality of images.

Abstract: Provided is an information-processing device including: a CPU; and a memory storing instructions for causing the information-processing device, when executed by the CPU, to: select M (M?N) images from N (N>1) images; generate a combined image by arranging the selected M images in M frames defined in advance, respectively; and determine a total assessment value in association with the generated combined image, the total assessment value including at least a linear sum of a selection assessment value being a linear sum of single image assessment values of the selected M images and a combination assessment value being a single image assessment value of the combined image.

Abstract: An information processing system configured to provide an electronic commerce platform. An information processing system is configured to acquire a descriptive text related to an item and at least one image related to the item; identify an object from the descriptive text; detect an object from each of the at least one image; and generate correspondence information based on correspondence between the object identified from the descriptive text and the object detected from the image.

Abstract: An information processing apparatus includes an acquiring unit configured to acquire an object image that includes an object, and a predicting unit configured to predict a class of the object by applying the object image acquired by the acquiring unit to a learning model. The learning model includes a main network (201) that extracts feature amounts regarding hierarchical classes of the object based on the object image, and a sub network (202) that outputs hierarchical classes of the object based on the feature amounts. The main network (201) includes extractors that extract the feature amounts. The sub network (202) includes classifiers that output classes of the object based on the feature amounts, and in the sub network, a higher-level classifier has a connection to one or more lower-level classifiers.

Abstract: To analyze images of more diverse graphs at higher speed and accuracy, disclosed herein is an information processing apparatus, comprising: a graph image acquisition unit configured to acquire a graph image; a graph classification unit configured to classify the graph image acquired by graph type; a probability map generation unit configured to generate, from the graph image, a probability map that is of a different type by the graph type; a component extraction unit configured to extract a plurality of components in the graph image based on the probability map generated by the probability map generation unit, and trace a graph with the plurality of components extracted; a value extraction unit configured to extract values of the components of the graph image extracted by the component extraction unit; and an output unit configured to output the values of the components extracted.

Abstract: Described herein is an information processing apparatus, including: a graph image acquisition unit configured to acquire a graph image; a graph classification unit configured to classify the graph image acquired by the graph image acquisition unit by graph type; a probability map generation unit configured to generate, from the graph image, a probability map that is of a different type by the graph type using a single neural network; a component extraction unit configured to extract a component in the graph image based on the probability map generated by the probability map generation unit; a value extraction unit configured to extract a value of the component of the graph image extracted by the component extraction unit; and an output unit configured to output the value of the component extracted by the value extraction unit.

Abstract: A first attitude estimation unit 16 estimates an attitude of an UAV 1 by performing a Visual-SLAM on the basis of detection values detected by an imaging unit 14, and a second attitude estimation unit 17 estimates an attitude of the UAV 1 on the basis of detection values detected by a rotation angle detection unit 15. And then a control unit 18 controls the attitude of the UAV 1 by utilizing a first attitude estimation result from the first attitude estimation unit 16 and a second attitude estimation result from the second attitude estimation unit 17 at a ratio based on positional information of the UAV 1.

Abstract: An information processing device 1 includes: a feature area detection unit 28 that acquires, by inputting a target image showing a fastening part that is an inspected target to a machine learning model, a feature area based on an estimated coordinate that includes a feature point relating to reference indication added to the fastening part and relates to a position of the feature point and a distribution of the estimated coordinate; an uncertainty evaluation unit 29 that determines whether the feature area satisfies a prescribed reference; and a fastening state determination unit 30 that determines a fastening state of the fastening part based on the estimated coordinate when the feature area satisfies the prescribed reference.

Abstract: Provided is an image processing system with at least one processor being configured to: calculate a color-adjusted pixel value vector, which is output of a transformation function defined by a formula including a term of a product of a pixel value vector and a transformation matrix, wherein the pixel value vector indicates a color of each of one or more adjustment pixels, which are at least the one or more target pixels out of pixels of the first image, and the transformation matrix has, as elements, a plurality of transformation parameters set to each of one or more adjustment pixels in a first parameter map, which is the second parameter map increased in resolution and which has the first resolution; and acquire a color-adjusted image including a color-adjusted pixel, the color-adjusted pixel having a color indicated by the color-adjusted pixel value vector.

Abstract: Provided are a display control system, a display control device, and a display control method, which are capable of appropriately controlling a display state of an image photographed by a camera included in an unmanned aerial vehicle. A sensing data acquisition module acquires an aerial vehicle photographing image photographed by the camera included in the unmanned aerial vehicle in flight. A determiner determines whether an operator of the unmanned aerial vehicle is able to visually recognize the unmanned aerial vehicle based on sensing data on at least one of the unmanned aerial vehicle or the operator. A display controller controls a display state of the aerial vehicle photographing image on a display depending on a result of determination by the determiner.

Abstract: [Problem] Provided is a novel information processing technology relating to a predetermined object in an image. [Solving Means] An information processing apparatus includes: an image acquisition unit that acquires an image used as teacher data for machine learning, the image being with one or a plurality of annotations for showing a position in the image at which a predetermined object is shown; an adjustment image generation unit that generates an adjustment image in which parameters of the image are adjusted; and a machine learning unit that generates a learning model for detecting the predetermined object in an image by performing machine learning using teacher data including the adjustment image.

Abstract: An information processing apparatus includes: a generating unit configured to generate, from a data group including training data elements given different correct labels, multiple training data sets in such a manner that the number of included training data elements changes successively, by successively selecting one or more training data elements from the data group; and a training unit configured to repeatedly train a learning model for machine learning by applying the training data sets to the learning model.

Abstract: An information processing device used for an electronic commerce platform includes one or more processors and one or more memories. At least one of the memories stores item data related to items registered in the electronic commerce platform. The item data includes data sets. The data sets each include data fields registered for one of the items. The data fields each include item information related to the one of the items. The processor is configured to execute an indexing process for one or more pieces of item information to obtain an index used to compare the one or more pieces of item information with each other.

Abstract: [Problem] The present disclosure has an object of correcting the orientation of a captured image captured during flight. [Solving Means] An information processing system includes: a captured image acquisition unit that acquires a captured image captured during flight by an imaging device mounted on an airplane; a relevant information acquisition unit that acquires information detected using a sensor mounted on the airplane; and an image correction unit that acquires, by correcting the captured image on a basis of information acquired using the relevant information, a correction image in which a predetermined direction in a state where the image is erected and a reference direction in the image are matched to each other.

Abstract: [Problem] Provided is a novel information processing technology relating to a predetermined object in an image. [Solving Means] An information processing apparatus includes: an image acquisition unit that acquires an image used as teacher data for machine learning, the image being with one or a plurality of annotations for showing a position in the image at which a predetermined object is shown; a region specification unit that specifies a region in which the one or the plurality of annotations satisfy a predetermined criterion in the image; an edge detection unit that preferentially detects edges in the specified region or a range set on a basis of the region; and an annotation correction unit that corrects the annotations so as to be along the detected edges.

Abstract: A transport system identifies an attribute of a ground on the basis of sensing data obtained by sensing the ground in a transport destination area, which is performed in flight by an Unmanned Aerial Vehicle transporting an article, and determines a region in the ground as an arrangement place of the article on the basis of the identified attribute.