Abstract: A vehicle display control device includes: a map information acquisition unit that acquires map data around a vehicle; and an item display unit that overlaps a virtual item on a predetermined travel road on the map data and that displays the virtual item in a display area in a vehicle cabin. The item display unit overlaps and displays the virtual item on the map data, thereby guiding a driver of the vehicle toward the travel road on which the virtual item is displayed.

Abstract: A maintenance information management device includes an information registration unit configured to register vehicle identification information and maintenance information in a memory for each vehicle, an information update unit configured to update the maintenance information after the maintenance of the vehicle by adding maintenance contents to the maintenance information registered in the memory, and an information providing unit configured to provide the maintenance information when a provision request for the maintenance information is accepted.

Abstract: An image processing device includes: a memory; and a processor coupled to the memory and configured to: acquire a first feature map output from a hidden layer by forward propagation of image data; acquire a plurality of second feature maps output from the hidden layer by forward propagation of each of a plurality of pieces of decoded data obtained by sequentially encoding the image data by using different quantization values and thereafter decoding the encoded image data; calculate a degree of influence of each block of the image data on a recognition result by backpropagating each error between the first feature map and the plurality of second feature maps; and determine a quantization value of each block when the image data is encoded.

Abstract: An analysis device includes: a memory; and a processor coupled to the memory and configured to: decide a first compression level based on a degree of influence of each area on a recognition result of a case where recognition processing is performed for each image data after a change in image quality; in a case where image data compressed at a second compression level according to the first compression level is decoded, perform the recognition processing for decoded data and calculate a recognition result; and determine at which compression level of the first compression level or the second compression level image data is compressed according to the calculated recognition result.

Abstract: An image processing device includes: a memory; and a processor coupled to the memory and configured to: calculate, in a case where an image quality of image data is changed, recognition accuracy of an object included in each piece of the image data that has been changed; change, in the image data, a region that includes the object to have an image quality with which the recognition accuracy becomes a predetermined allowable limit and to change a region other than the region that includes the object to have an image quality with which the recognition accuracy becomes less than the predetermined allowable limit; and input, into an encoder, the image data that has been changed.

Abstract: A bit rate control system includes: a memory; and a processor coupled to the memory and configured to: perform an image recognition process on a frame to be processed in video while changing image quality to specify the image quality at which recognition accuracy of an object included in the frame to be processed reaches an allowable limit; calculate a first quantization step that corresponds to the specified image quality; determine whether or not overflow occurs in a virtual buffer when encoding processing is performed on the frame to be processed by using the calculated first quantization step; and exercise control to perform the encoding processing on the frame to be processed by using the calculated first quantization step when the overflow is determined not to occur.

Abstract: A method includes: generating a refine image having a maximized correct label score of inference from an incorrect image from which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of a plurality of pixels of the incorrect image, the second map indicating a degree of attention for each local region in the refine image, the each local region being a region that has drawn attention by the neural network; and specifying a set of pixels that cause incorrect inference in the incorrect image by calculating a pixel value of the third map for each set of pixels, wherein the map generating processing adjusts the second map based on appearance frequency of each degree of attention.

Abstract: An analysis device includes a processor configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process has been executed, while gradually changing recognition accuracy of the images generated by the generative model on which the first learning process has been executed, to desired recognition accuracy; acquire each piece of information on back-error propagation calculated by executing the image recognition process, for the images with each level of the recognition accuracy generated through a course of the second learning process; and generate evaluation information indicating each of image parts that cause erroneous recognition at each level of the recognition accuracy, based on the acquired each piece of the information on the back-error propagation.

Abstract: An analysis device includes: a memory; and a processor coupled to the memory and configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process which has been executed such that recognition accuracy of the images generated by the generative model on which the first learning process has been executed matches desired recognition accuracy; acquire information on back-error propagation calculated by executing the image recognition process, for the images with the desired recognition accuracy generated by executing the second learning process; and generate evaluation information that indicates image parts that cause over-detection at the desired recognition accuracy, based on the acquired information on the back-error propagation.

Abstract: An analysis method implemented by a computer includes: generating a refine image by changing an incorrect inference image such that a correct label score of inference is maximized, the incorrect inference image being an input image when an incorrect label is inferred in an image recognition process; and narrowing, based on a score of a label, a predetermined region to specify an image section that causes incorrect inference, the score of the label being inferred by inputting to an inferring process an image obtained by replacing the predetermined region in the incorrect inference image with the refine image.

Abstract: A data processing device includes: a memory; and a processor coupled to the memory and configured to: in a case where a compression level is designated based on a degree of influence of each block on a recognition result when a recognition process is performed on image data, generate compressed data by performing a compression process on the image data by using the compression level; and in a case where the recognition result when the recognition process is performed on decoded data obtained by decoding the compressed data satisfies a predetermined condition, correct a block that corresponds to a recognition target, in a direction of raising the compression level.

Abstract: An analysis device includes: a memory; and a computer coupled to the memory and configured to: store information that indicates a degree of influence of each area of each piece of decoded data on recognition results and is calculated by performing a recognition process on the decoded data obtained by decoding each piece of compressed data when a compression process is performed on image data at different compression levels; and designate the compression levels for each area of the image data, based on the information that corresponds to the different compression levels and indicates the degree of influence of each area of each piece of the decoded data on the recognition results.

Abstract: A method includes: generating a refine image having a maximized correct label score of inference from an incorrect image by which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of plural pixels of the incorrect image, the second map indicating a degree of attention for each local region in the refine image, the each local region being a region that has drawn attention at the time of inference by the neural network, and the third map indicating a degree of importance for each pixel for inferring a correct label; and specifying an image section based on a pixel value of the third map, the image section corresponding to a region causing incorrect inference in the incorrect image.

Abstract: A method includes: generating a refine image from an incorrect image from which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of plural pixels in the incorrect image, the second map indicating a degree of attention for each local region in the refine image, each local region being a region that has drawn attention at the time of inference by the neural network, and the third map indicating a degree of importance for each pixel for inferring a correct label; and obtaining an added value for respective divided region in the third map by summing pixel values within the respective divided region, the respective divided region being a region divided according to a predetermined index.

Abstract: An analysis method executed by a computer includes acquiring a refine image that maximizes a score for inferring a correct label by an inferring process using a trained model, the refine image being generated from an input image used when an incorrect label is inferred; generating a map indicating a region of pixels having the same or similar level of attention degree related to inference in the inferring process, of a plurality of pixels in the generated refine image, based on a feature amount used in the inferring process; extracting an image corresponding to a pixel region whose level in the generated map is a predetermined level, from calculated images calculated based on the input image and the refine image; and generating an output image that specifies a portion related to an inference error in the inferring process, among the calculated images, based on image processing on the extracted image.

Abstract: An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: partition pixel values in a unit of row of an input image into a plurality of sections and allocates threads to the respective sections of the row, the threads being enabled to run in parallel by a processor; calculate, with each of the threads allocated in each row, distances each from a pixel having a certain value in the corresponding section of the row in the input image, and generates a first distance image which stores values indicating the distances; and calculate, with each of the threads allocated in each row, a first boundary value indicating a distance from a pixel having the certain value in another section of each row, by using a calculation result of the first boundary value in the another section of each row.

Abstract: A method includes: generating a refine image from an incorrect image from which an incorrect label is inferred by a neural network; generating a third map by superimposing a first map and a second map, the first map indicating pixels to each of which a change is made in generating the refine image, of plural pixels in the incorrect image, the second map indicating a degree of attention for each local region in the refine image, each local region being a region that has drawn attention at the time of inference by the neural network, and the third map indicating a degree of importance for each pixel for inferring a correct label; and obtaining an added value for respective divided region in the third map by summing pixel values within the respective divided region, the respective divided region being a region divided according to a predetermined index.