Abstract: An information processing apparatus (1) includes control means (30), data input means (10) for inputting sequence data, a machine learning model (21) that generates new sequence data based on the sequence data input by the data input means (10), and sequence data selecting means (10) for selecting, when new sequence data is generated by the machine learning model (21), target sequence data for changing the sequence data and/or context sequence data for not changing the sequence data. The control means (30) (i) generates new target sequence data that interpolates at least two sequence data already generated by the machine learning model or (ii) generates different new sequence data for the sequence data already generated by the machine learning model.

Abstract: An information processing apparatus 100 according to the present disclosure includes an extraction unit 131 that extracts first data from an element constituting first content, and a model generation unit 132 that generates a learned model that has a first encoder 50 that calculates a first feature quantity which is a feature quantity of first content, and a second encoder 55 that calculates a second feature quantity which is a feature quantity of the extracted first data.

Abstract: An information processing apparatus according to an embodiment of the present technology includes a detection unit, an estimation unit, and a prediction unit. The detection unit detects a target object from an input image. The estimation unit estimates a posture of the detected target object. The prediction unit predicts an action of the target object on a basis of the estimated posture.

Abstract: An information processing apparatus (100) according to the present disclosure includes an acquiring unit (132) that uses a trained encoder to acquire a feature value of each set of partial data forming first content; a calculating unit (133) that calculates a relative feature value that is a relative feature value between sets of partial data from the acquired feature values of the sets of partial data to calculate a relative feature value sequence indicating a feature of a structure of the first content; and a generating unit (134) that generates second content based on the relative feature value sequence of the first content and a feature value of optional data.

Abstract: An information processing apparatus 100 according to the present disclosure includes an extraction unit 131 that extracts first data from an element constituting first content, and a model generation unit 132 that generates a learned model that has a first encoder 50 that calculates a first feature quantity which is a feature quantity of first content, and a second encoder 55 that calculates a second feature quantity which is a feature quantity of the extracted first data.

Abstract: A damage reduction device according to an embodiment of the present technology includes an input unit, a prediction unit, a recognition unit, and a determination unit. The input unit inputs status data regarding a status in a moving direction of a moving body apparatus. The prediction unit predicts a collision with an object in the moving direction on the basis of the status data. The recognition unit recognizes whether the object includes a person. The determination unit determines, when the collision with the object is predicted and it is recognized that the object includes a person, a steering direction of the moving body apparatus in which a collision with the person is avoidable, on the basis of the status data.

Abstract: An information processing method including generating, by using a plurality of features not in a concatenating relationship and using a trained model, new data obtained from the plurality of features having alterations, in which, when having received an input of the plurality of features, the trained model outputs the plurality of features having alterations.

Abstract: An information processing method is an information processing method including generating an output track by using an input track including a plurality of first information elements provided over a certain period or a certain section and a learned model (21), in which the output track includes a first track that is a same track as the input track or a changed track, and a second track including a plurality of second information elements provided over the certain period or the certain section, and the learned model (21) is a learned model generated by using training data so as to output output data corresponding to the output track when input data corresponding to the first track is input.

Abstract: The present disclosure relates to an information processing apparatus, an information processing method, and a program that make it possible to adjust commonness and eccentricity of automatically generated content by likelihood exploration while satisfying reality. Input content including a sequence of data is encoded to be converted into a latent variable, the latent variable is decoded to reconfigure output content, a loss function is calculated on the basis of a likelihood of the input content which is an input sequence, a gradient of the loss function is lowered to update the latent variable, and the updated latent variable is decoded to reconfigure output content. The present invention can be applied to an automatic content generation device.

Abstract: Methods and apparatus for analyzing embryonic development images. The method comprises obtaining a plurality of embryonic development images captured in a time series, determining, for at least one of the plurality of embryonic development images, a time series of evaluation values for each of a plurality of evaluation items associated with the plurality of embryonic development images, and evaluating a characteristic of cells represented in one or more of the plurality of embryonic development images based, at least in part, on the time series of evaluation values for the plurality of evaluation items.

Abstract: A damage reduction device according to an embodiment of the present technology includes an input unit, a prediction unit, a recognition unit, and a determination unit. The input unit inputs status data regarding a status in a moving direction of a moving body apparatus. The prediction unit predicts a collision with an object in the moving direction on the basis of the status data. The recognition unit recognizes whether the object includes a person. The determination unit determines, when the collision with the object is predicted and it is recognized that the object includes a person, a steering direction of the moving body apparatus in which a collision with the person is avoidable, on the basis of the status data.

Abstract: A damage reduction device according to an embodiment of the present technology includes an input unit, a prediction unit, a recognition unit, and a determination unit. The input unit inputs status data regarding a status in a moving direction of a moving body apparatus. The prediction unit predicts a collision with an object in the moving direction on the basis of the status data. The recognition unit recognizes whether the object includes a person. The determination unit determines, when the collision with the object is predicted and it is recognized that the object includes a person, a steering direction of the moving body apparatus in which a collision with the person is avoidable, on the basis of the status data.

Abstract: An information processing apparatus 100 according to the present disclosure includes an extraction unit 131 that extracts first data from an element constituting first content, and a model generation unit 132 that generates a learned model that has a first encoder 50 that calculates a first feature quantity which is a feature quantity of first content, and a second encoder 55 that calculates a second feature quantity which is a feature quantity of the extracted first data.

Abstract: An information processing apparatus (100) according to the present disclosure includes an acquiring unit (132) that uses a trained encoder to acquire a feature value of each set of partial data forming first content; a calculating unit (133) that calculates a relative feature value that is a relative feature value between sets of partial data from the acquired feature values of the sets of partial data to calculate a relative feature value sequence indicating a feature of a structure of the first content; and a generating unit (134) that generates second content based on the relative feature value sequence of the first content and a feature value of optional data.

Abstract: An information processing apparatus according to an embodiment of the present technology includes a detection unit, an estimation unit, and a prediction unit. The detection unit detects a target object from an input image. The estimation unit estimates a posture of the detected target object. The prediction unit predicts an action of the target object on a basis of the estimated posture.

Abstract: An information processing apparatus according to an embodiment of the present technology includes a detection unit, an estimation unit, and a prediction unit. The detection unit detects a target object from an input image. The estimation unit estimates a posture of the detected target object. The prediction unit predicts an action of the target object on a basis of the estimated posture.

Abstract: Provided is a control device including a control unit that outputs information that controls output of scent on the basis of a result of recognizing a hand.

Abstract: Methods and apparatus for analyzing embryonic development images. The method comprises obtaining a plurality of embryonic development images captured in a time series, determining, for at least one of the plurality of embryonic development images, a time series of evaluation values for each of a plurality of evaluation items associated with the plurality of embryonic development images, and evaluating a characteristic of cells represented in one or more of the plurality of embryonic development images based, at least in part, on the time series of evaluation values for the plurality of evaluation items.

Abstract: A polarized image acquisition unit 20 acquires polarized images in a plurality of polarization directions. A reflection information generation unit 30 generates reflection information indicating reflection components from the polarized images in the plurality of polarization directions acquired by the polarized image acquisition unit 20. A reflection information using unit 40 uses the reflection information generated by the reflection information generation unit 30 to acquire an image of a viewed object appearing in the polarized images. A depth estimation unit estimates a depth value of a reflective surface area and acquires a position of the viewed object on the basis of an image of the viewed object appearing in the reflective surface area and the estimated depth value. Therefore, the viewed object positioned in, for example, an area of a blind spot can be easily checked.

Abstract: A control apparatus includes an input unit and a control unit. To the input unit, a pickup image of a camera provided to an own vehicle is input. The control unit detects a mirror provided to a different vehicle that exists in front of the own vehicle from the input pickup image, detects a person from a mirror image of the detected mirror, and recognizes a state of the person from an image of the detected person. Further, the control unit performs an alerting process or a control process for the own vehicle to prevent an accident of the own vehicle or the different vehicle in accordance with the recognized state of the person.