Abstract: According to an aspect of the present invention, there is provided a terminal apparatus including an operator that receives information indicating a position in a body of a subject from a user, in which biological information corresponding to the position received by the operator is output on the basis of biological information obtained by a plurality of sensors of which positions are substantially fixedly maintained with respect to the body of the subject.

Abstract: A learning device calculates a feature of each data included in a pair of datasets in which two modalities among a plurality of modalities are combined, using a model that receives data on a corresponding modality among the modalities and outputs a feature obtained by mapping the received data into an embedding space. The learning device then selects similar data similar to each target data that is data on a first modality in a first dataset of the datasets, from data on a second modality included in a second dataset of the datasets. The learning device further updates a parameter of the model such that the features of the data in the pair included in the first and the second datasets are similar to one another, and the feature of data paired with the target data is similar to the feature of data paired with the similar data.

Abstract: A learning device calculates an image feature using a model (image encoder) that receives an image and outputs the image feature obtained by mapping the image into a latent space. The learning device calculates an audio feature using a model (audio encoder) that receives a speech in a predetermined language and outputs the audio feature obtained by mapping the speech into the latent space, and that includes a neural network provided with a self-attention mechanism. The learning device updates parameters of the models used by an image feature calculation unit and an audio feature calculation unit such that the image feature of a first image is similar to the audio feature of a speech corresponding to the first image.

Abstract: The purpose of the present invention is to enable learning of a neural network for extracting features of images having high robustness from an undiscriminating image region while minimizing the number of parameters of a pooling layer. A parameter learning unit 130 learns parameters of each layer in a convolutional neural network configured by including a fully convolutional layer for performing convolution of an input image to output a feature tensor of the input image, a weighting matrix estimation layer for estimating a weighting matrix indicating a weighting of each element of the feature tensor, and a pooling layer for extracting a feature vector of the input image based on the feature tensor and the weighting matrix.

Abstract: The present invention enables detection of a sponsorship credit display segment in a broadcast program with higher precision.

Abstract: To enable extraction of an area related to a sponsor credit from a video including the sponsor credit of a television broadcast or the like without generating learning data for each form of various kinds of sponsor credits. A detection device (10) according to the present invention includes a detection unit (19) that associates a still image including a prescribed character or figure from a preliminary video or a still image not including the prescribed character or figure with a sound signal including the prescribed sound acquired from the preliminary video so as to detect a desired scene as an area that includes at least one of the prescribed character or figure and the prescribed sound from the target video.

Abstract: A detection device detecting a scene related to a sponsor credit included in a commercial message from a target video is provided. The detection device comprises a detection unit that associates, from a preliminary video, a still image related to the sponsor credit with an audio signal related to the sponsor credit included other than in a frame or an audio signal configuring the commercial message so as to detect the scene related to the sponsor credit from the target video.

Abstract: A depth estimation method using a depth estimator trained to output a depth map of a depth provided to each pixel of an input image, in which: the depth estimator includes a pair of a first convolutional layer and a second convolutional layer coupled to each other and configured to, when having received, as input, a tensor obtained by applying predetermined conversion to an input image, apply a two-dimensional convolution operation to the tensor and output the tensor to which the two-dimensional convolution operation is applied; the first convolutional layer is a convolutional layer including a first kernel of a shape having lengths in a first direction and a second direction, the first direction being one of a vertical direction and a horizontal direction, the second direction being different from the first direction, the length in the second direction being longer than the length in the first direction; and the second convolutional layer is a convolutional layer including a second kernel of a shape having l

Abstract: The present invention enables detection of a sponsorship credit display in a broadcast program with higher precision. A sponsorship credit display detection device 100 according to the present invention includes: a CM segment detection unit 120 that extracts a cut point, which is a time point where a frame in which the volume of an audio signal of a broadcast program is less than a volume threshold value and the amount of change from a previous frame is at least a pixel change threshold value is played, and detects a CM segment by comparing an interval of the extracted cut point with a CM defined length; a sponsorship credit display segment estimation unit 130 that estimates, as a sponsorship credit display segment, a predetermined time period before or after at least one continuous CM segment detected by the CM segment detection unit 120; and an output unit 140 that outputs information indicating the sponsorship credit display segment.

Abstract: A signal retrieval device includes a modification unit and a signal retrieval unit. The modification unit modifies a value of an attribute of a target represented by an input signal or a stored signals stored in a signal storage unit or a value of an attribute relating to a signal generation source of the input signal to acquire a plurality of modified values of the attribute. The signal retrieval unit retrieves a stored signal of the stored signals similar to the input signal using the input signal or the stored signals in which the attribute is modified according to each of the plurality of modified values of the attribute acquired by the modification unit.

Abstract: Low-dimensional feature values with which semantic factors of content are ascertained are generated from relevance between sets of two types of content. Based on a relation indicator indicating a pair of groups indicating which groups are related to first types of content groups among second types of content groups, an initial feature value extracting unit 11 extracts initial feature values of the first type of content and the second type of content. A content pair selecting unit 12 selects a content pair by selecting one first type of content and one second type of content from each pair of groups indicated by the relation indicator. A feature value conversion function generating unit 13 generates feature conversion functions 31 of converting the initial feature values into low-dimensional feature values based on the content pair selected from each pair of groups.

Abstract: Clustering can be performed using a self-expression matrix in which noise is suppressed. A self-expression matrix is calculated that minimizes an objective function that is for obtaining, from among matrices included in a predetermined matrix set, a self-expression matrix whose elements are linear weights when data points in a data set are expressed by linear combinations of points, the objective function being represented by a term for obtaining the residual between data points in the data set and data points expressed by linear combinations of points using the self-expression matrix, a first regularization term that is multiplied by a predetermined weight and is for reducing linear weights of the data points that have a large Euclidean norm in the self-expression matrix, and a second regularization term for the self-expression matrix. A similarity matrix defined by the calculated self-expression matrix is then calculated.

Abstract: An image adjustment device includes: an illumination component derivation unit that derives an illumination component of a grayscale image; a reflectance component derivation unit that derives a reflectance component image that is a resulting image in which the illumination component is removed from the grayscale image; a contrast component derivation unit that derives a contrast component based on a contrast value between a pixel of the reflectance component image and a peripheral area of the pixel; a histogram derivation unit that derives a luminance histogram of the grayscale image weighted according to the contrast value for each pixel of the contrast component; a conversion function derivation unit that derives a luminance conversion function for converting a luminance such that a luminance histogram of a converted grayscale image in which the grayscale image is converted by the luminance conversion function and a predetermined histogram are matched with or similar to each other; and a luminance conversi

Abstract: To provide database generation techniques that can accurately and efficiently generate a database useable in text-based sound signal search. A sound signal database generation apparatus includes: a latent variable generation unit that generates, from a sound signal, a latent variable corresponding to the sound signal using a sound signal encoder; a data generation unit that generates a natural language representation corresponding to the sound signal from the latent variable and a condition concerning an index for a natural language representation using a natural language representation decoder; and a sound signal database generation unit that generates a record including the natural language representation corresponding to the sound signal and the sound signal from the natural language representation corresponding to the sound signal and the sound signal, and generates a sound signal database made up of the record.

Abstract: A learning device calculates a feature of each data included in a pair of datasets in which two modalities among a plurality of modalities are combined, using a model that receives data on a corresponding modality among the modalities and outputs a feature obtained by mapping the received data into an embedding space. The learning device then selects similar data similar to each target data that is data on a first modality in a first dataset of the datasets, from data on a second modality included in a second dataset of the datasets. The learning device further updates a parameter of the model such that the features of the data in the pair included in the first and the second datasets are similar to one another, and the feature of data paired with the target data is similar to the feature of data paired with the similar data.

Abstract: A learning device calculates an image feature using a model (image encoder) that receives an image and outputs the image feature obtained by mapping the image into a latent space. The learning device calculates an audio feature using a model (audio encoder) that receives a speech in a predetermined language and outputs the audio feature obtained by mapping the speech into the latent space, and that includes a neural network provided with a self-attention mechanism. The learning device updates parameters of the models used by an image feature calculation unit and an audio feature calculation unit such that the image feature of a first image is similar to the audio feature of a speech corresponding to the first image.

Abstract: Information related to CMs included in a broadcast program can be automatically added. A CM information generation device 100 includes: a CM section detection unit 120 that detects one or more CM sections within a broadcast program by comparing the volume of the broadcast program with a volume threshold; a CM detection list generation unit 150 that generates a CM detection list describing company names of companies that have advertised detected CMs, which are CMs in the CM sections detected by the CM section detection unit 120, by cross-referencing the detected CMs with CM masters that have been associated with company names of advertisers in advance; a company name list generation unit 170 that generates a company name list describing company names that are specified by a sponsorship credit display indicating sponsors of the broadcast program; and a CM information generation unit 180 that generates CM information related to the detected CMs by comparing the CM detection list with the company name list.

Abstract: To provide sound signal search techniques that can search for sound signals without tagging with text data. A sound signal search apparatus includes: a recording unit that records a sound signal database made up of records each including a latent variable corresponding to a sound signal and the sound signal, the latent variable being generated from the sound signal with a sound signal encoder; a latent variable generation unit that generates, from a natural language representation being input (hereinafter referred to as an input natural language representation), a latent variable corresponding to the input natural language representation using a natural language representation encoder; and a search unit that determines sound signals corresponding to the input natural language representation as a search result from the latent variable corresponding to the input natural language representation using the sound signal database.

Abstract: To provide techniques for generating, from a sound signal, a natural language representation corresponding to the sound signal while controlling a predetermined index for a natural language representation. A data generation apparatus 200 includes: a latent variable generation unit 210 that generates, from a sound signal, a latent variable corresponding to the sound signal using an encoder; and a data generation unit 220 that generates a natural language representation corresponding to the sound signal from the latent variable and a condition concerning an index for the natural language representation using a decoder.

Abstract: The chroma of a specific color can be emphasized with respect to an object without affecting the color of illumination light. An illumination light generator generates illumination light to be radiated to the object by adding or subtracting, to or from a reference illumination light spectrum that is an illumination spectrum serving as a reference, an element spectrum in accordance with designated conditions with respect to chroma adjustment from among a plurality of element spectra that are spectra for being added or subtracted to or from the reference illumination light spectrum and for performing chroma emphasis of a specific color without affecting an illumination light color.