Abstract: An objective of the present invention is to achieve greater efficiency in searches for illegal (illegitimate) content. The illegitimate content relates to content posted by an unauthorized user without a legitimate ownership of the content. An illegitimate content search device according to the present invention comprises: a content profile acquisition part for acquiring a profile including a posting history of illegitimate content posted by a user having posted candidate content being potentially illegitimate content; and a matching priority calculation part for calculating, on the basis of the profile, the priority of the candidate content with regard to determining whether a plurality of pieces of content is illegitimate content, and elevating the priority of the illegitimate content with a history of having posted the illegitimate content higher than if content without the history.

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

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: 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: Performance of an encoding process and a decoding process for a sound signal is enhanced. A representative value calculating part 110 calculates, for each frequency section by a plurality of samples fewer than the number of frequency samples of a sample sequence of a frequency domain signal corresponding to an input acoustic signal, from the sample sequence of the frequency domain signal, a representative value of the frequency section from sample values of samples included in the frequency section, for each of predetermined time sections. A signal companding part 120 obtains, for each of the predetermined time sections, a frequency domain sample sequence obtained by multiplying a weight according to a function value of the representative value by a companding function for which an inverse function can be defined and each of the samples corresponding to the representative value in the sample sequence of the frequency domain signal, as a sample sequence of a weighted frequency domain signal.

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: In a depth estimation device, a generation unit generates a predetermined attractive sound in a space to be measured. A sound pickup unit picks up an acoustic signal for a predetermined time period corresponding to a time period before and after a time of generation of the attractive sound. An estimation unit extracts a feature representing time-frequency information obtained through analysis of the acoustic signal, on the basis of the acoustic signal, and inputs the extracted feature representing the time-frequency information to a depth estimator and generates an estimated depth map for the space to be measured, the depth estimator being composed of one or more convolution operations and being learned so as to output an estimated depth map, in which a depth is assigned to each of pixels of an image representing the space to be measured, when a feature representing the time-frequency information is input.

Abstract: An acquiring unit of a depth estimation apparatus acquires an input image. In addition, a depth map generating unit inputs the input image acquired by the acquiring unit into a depth estimator for generating, from an image, a depth map in which a depth of a space that appears on the image is imparted to each pixel of the image, and generates an estimated depth map that represents a depth map corresponding to the input image. The depth estimator is a model having been learned in advance so as to reduce, with respect to each error between a depth of the estimated depth map and a depth of a correct-answer depth map that presents the depth map of a correct answer, a value of a loss function set such that a degree of increase of a loss value with respect to a pixel at which the error is larger than a threshold is smaller than a degree of increase of a loss value with respect to a pixel at which the error is equal to or smaller than the threshold.

Abstract: A CM section within a broadcast program is detected with high accuracy. A CM section detection device 100 includes: a CM section detection unit 120 that detects one or more CM sections by comparing a volume of a broadcast program with a volume threshold, and generates detected CM sections representing the CM sections that have been detected; and a CM section correction unit 140 that corrects the detected CM sections based on a sponsorship credit display section that is a section which is included in the broadcast program and in which a sponsorship credit indicating a sponsor of the broadcast program is displayed.

Abstract: A search apparatus for searching media data for a target region that matches query data includes a first feature extraction unit configured to extract a first feature vector from the query data using a first trained neural network; a second feature extraction unit configured to obtain a first region from the media data and extract a second feature vector from the first region using a second trained neural network; a localization unit configured to determine a candidate for the target region using a third trained neural network, based on the first feature vector, the second feature vector, and the first region or a location of the first region; and a control unit configured to repeat the operations of the second feature extraction unit and the localization unit until a predetermined condition is satisfied, by using the determined candidate for the target region as the first region to be used by the second feature extraction unit.

Abstract: A credit segment identifying device includes an extracting unit which extracts, from a first speech signal, a plurality of first partial speech signals which are each a part of the first speech signals and shifted from each other in time direction and an identifying unit which identifies a credit segment in the first speech signal by determining whether each of the first partial speech signals includes a credit according to an association between each of second partial signals extracted from a second speech signal and the presence/absence of a credit, so that credit segments can be identified more efficiently.

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: An identifier of query content is accurately determined. A content determining device 100 includes an input unit 2 that inputs query content, a storage unit 1 that stores a plurality of pieces of master content, and a content determining unit 4 that determines a region where feature values of two pieces of master content out of the plurality of pieces of master content do not match each other, calculates a matching feature count which is a count of feature values of the region that match feature values in a corresponding region of the query content, for each of the two pieces of master content, and determines an identifier of the query content on the basis of the matching feature count of each of the pieces of master content.