Abstract: An information processing method includes the following executed by a computer: acquiring a first image and object data of an object appearing in the first image, extracting a portion of the first image that corresponds to a difference between the object data and an object detection result obtained by inputting the first image to a trained model, the trained model receiving an image as input to output an object detection result, acquiring a second image that includes a portion corresponding to the same object data as object data corresponding to the extracted portion of the first image, reflecting an image based on the extracted portion of the first image in the portion of the acquired second image that corresponds to the same object data, and generating training data for the trained model.

Abstract: An information processing method includes acquiring a first prediction result by inputting evaluation data to a first model; determining an anomaly in the first prediction result based on the first prediction result and reference information; acquiring a second model based on the determination result; acquiring a second prediction result by inputting the evaluation data to the second model; determining an anomaly in the second prediction result based on the second prediction result and the reference information; acquiring a third model based on the determination result; acquiring a third prediction result by inputting the evaluation data to the third model; determining an anomaly in the third prediction result based on the third prediction result and the reference information; and if the anomaly in the third prediction result is recognized as being identical to the anomaly in the first prediction result, outputting information about a training limit of the first model.

Abstract: An information processing method includes the following executed by a computer: acquiring a first image and object data of an object appearing in the first image, extracting a portion of the first image that corresponds to a difference between the object data and an object detection result obtained by inputting the first image to a trained model, the trained model receiving an image as input to output an object detection result, acquiring a second image that includes a portion corresponding to the same object data as object data corresponding to the extracted portion of the first image, reflecting an image based on the extracted portion of the first image in the portion of the acquired second image that corresponds to the same object data, and generating training data for the trained model.

Abstract: An information processing method is executed by a computer and includes acquiring a first recognition result that is output as a result of inputting sensing data to a first recognition model trained through machine learning, acquiring reference data for the sensing data, determining a difference in class of a recognition target between the first recognition result and the reference data, generating an additional class for the first recognition model when the difference satisfies a predetermined condition, and outputting the sensing data or processed data obtained by processing the sensing data as training data for the additional class.

Abstract: An information processing apparatus is configured to cause a first recognizer to execute a first recognition process that takes sensor information as input, and a second recognizer to execute a second recognition process that takes the sensor information as input, the second recognizer being configured to operate under different capability conditions from the first recognizer; determine one of a transmission necessity and a transmission priority of the sensor information depending on a difference between a first recognition result of the first recognition process and a second recognition result of the second recognition process; and transmit the sensor information to a server apparatus according to the determined one of the transmission necessity and the transmission priority.

Abstract: An information processing method includes, by using a computer: acquiring an annotation skill of an operator who provides annotation information to image; acquiring annotation-target image having an image feature identified based on the annotation skill; acquiring an annotation result attained by the operator for the annotation-target image; and updating the annotation skill using the annotation result and reference annotation information corresponding to the image feature.

Abstract: An information processing method includes the following performed using a computer: acquiring images created through capturing at the same time, and positions and capturing directions of image capturing devices that created respectively each of the images; acquiring object detection results obtained respectively through object detection processes performed using each of the images; and performing a training data selection process using the images, the positions and the capturing directions, and the object detection results acquired. The training data selection process includes: calculating a common region that is seen in the images in common, based on the positions and the capturing directions; determining a degree of agreement among the object detection results in the common region; and selecting an image to be used as training data from among the images, according to the degree of agreement.

Abstract: An information processing method includes acquiring a first prediction result by inputting evaluation data to a first model; determining an anomaly in the first prediction result based on the first prediction result and reference information; acquiring a second model based on the determination result; acquiring a second prediction result by inputting the evaluation data to the second model; determining an anomaly in the second prediction result based on the second prediction result and the reference information; acquiring a third model based on the determination result; acquiring a third prediction result by inputting the evaluation data to the third model; determining an anomaly in the third prediction result based on the third prediction result and the reference information; and if the anomaly in the third prediction result is recognized as being identical to the anomaly in the first prediction result, outputting information about a training limit of the first model.

Abstract: An information processing method includes the following performed using a computer: acquiring images created through capturing at the same time, and positions and capturing directions of image capturing devices that created respectively each of the images; acquiring object detection results obtained respectively through object detection processes performed using each of the images; and performing a training data selection process using the images, the positions and the capturing directions, and the object detection results acquired. The training data selection process includes: calculating a common region that is seen in the images in common, based on the positions and the capturing directions; determining a degree of agreement among the object detection results in the common region; and selecting an image to be used as training data from among the images, according to the degree of agreement.

Abstract: An information processing method includes, by using a computer: acquiring an annotation skill of an operator who provides annotation information to image; acquiring annotation-target image having an image feature identified based on the annotation skill; acquiring an annotation result attained by the operator for the annotation-target image; and updating the annotation skill using the annotation result and reference annotation information corresponding to the image feature.

Abstract: An information processing apparatus is configured to cause a first recognizer to execute a first recognition process that takes sensor information as input, and a second recognizer to execute a second recognition process that takes the sensor information as input, the second recognizer being configured to operate under different capability conditions from the first recognizer; determine one of a transmission necessity and a transmission priority of the sensor information depending on a difference between a first recognition result of the first recognition process and a second recognition result of the second recognition process; and transmit the sensor information to a server apparatus according to the determined one of the transmission necessity and the transmission priority.

Abstract: Provided are an information processing method, an information processing apparatus, and a program that can increase a diversity of learning data for configurations or techniques of unspecified devices. The information processing method includes: obtaining sensor data obtained by a sensor installed in a vehicle, and at least one type of traveling data of the vehicle; associating the sensor data and the at least one type of traveling data with each other; determining a degree of difference of the at least one type of traveling data from the at least one type of one or more traveling data associated with one or more sensor data; and selecting the sensor data as learning data according to the degree of difference.

Abstract: This stuffed article (1) of the present invention is made of ticking (2a, 2b) filled with a stuffing. The stuffing is a long-fiber wad (8) formed by integrating an effect yarn with a core yarn. The effect yarn is opened to form loop-like fibers. A plurality of strings of the long-fiber wad (8) are arranged in parallel in at least one direction, and sewn to the ticking so as to be integrated with the ticking. The long-fiber wad in the stuffed article is fixed to the ticking, and the effect yarn is integrated with the core yarn. Therefore, the present invention can provide a stuffed article where the displacement of the long-fiber wad is limited even after repeated washing, the wad is less concentrated, and the bulkiness is high.

Abstract: This stuffed article (1) of the present invention is made of ticking (2a, 2b) filled with a stuffing. The stuffing is a long-fiber wad (8) formed by integrating an effect yarn with a core yarn. The effect yarn is opened to form loop-like fibers. A plurality of strings of the long-fiber wad (8) are arranged in parallel in at least one direction, and sewn to the ticking so as to be integrated with the ticking. The long-fiber wad in the stuffed article is fixed to the ticking, and the effect yarn is integrated with the core yarn. Therefore, the present invention can provide a stuffed article where the displacement of the long-fiber wad is limited even after repeated washing, the wad is less concentrated, and the bulkiness is high.

Abstract: The digital video data test system includes a semiconductor device to be tested and a digital video data test device. In the semiconductor device, a clock frequency division section divides the frequency of a digital video clock to generate a frequency-divided clock. A timing signal generation section generates a timing signal synchronizing with the frequency-divided clock using a sync signal in digital video data. A code holding section outputs a generated code generated by a code generation section to the digital video data test device in synchronization with the timing signal and the frequency-divided clock.