Abstract: To accurately identify a region corresponding to a position where an object is preset in an image, an object region identification device acquires a first frame image and a second frame image that are temporally successive. The device inputs the first frame image to a model that identifies an object in an image and acquires position information, the position affecting identification of an object in the first frame image. The device acquires motion information indicating a motion of the object in the first frame image based on the first frame image and the second frame image. The device generates, based on the acquired position information and motion information, region information indicating a region in the first frame image, the region corresponding to a position where the object is present.

Abstract: Calculating a first loss based on an output of a learning model and a target output, when multi-label query data is input to a learning model. Acquiring a feature amount of the query data and a feature amount of support data corresponding to the query data, which are calculated based on a parameter of the learning model. Calculating a second loss based on the feature amount of the query data and the feature amount of the support data. Adjusting the parameter based on the first loss and the second loss.

Abstract: To easily and correctly determine substantial identity of a plurality of images in each of which an object is placed, provided is an image judgement method including steps of obtaining first object data from a first image with a use of an R-CNN 12, which is a first machine learning model, where the first object data indicates an attribute and a layout of an object in the first image, obtaining second object data from a second image with a use of the R-CNN 12, where the second object data indicates an attribute and a layout of an object in the second image, and determining substantial identity of the first image and the second image based on the first object data and the second object data with a use of a CNN, which is a second machine learning model.

Abstract: The information processing device acquires a probability image representing a probability of an existence of a character in each of pixels included in a target image including a plurality of characters based on the target image, estimates positions of respective character images included in the target image based on the acquired probability image, classifies the plurality of character images into a plurality of groups based on the estimated positions, acquires a plurality of recognition target images which is generated so as to correspond to the plurality of groups, and includes the plurality of character images respectively belonging to the corresponding groups, and recognizes the plurality of characters from each of the recognition target images.

Abstract: The information processing device obtains a character string image which includes a plurality of characters, and which includes the characters arranged in an arrangement direction, obtains a probability image representing a probability of an existence of a character in each of pixels included in the character string image, obtains a plurality of character regions in which the characters are estimated to respectively exist in the character string image based on the probability image, obtains an additional character region which is located in the character string image, and which does not overlap the plurality of character regions based on a determination result on whether or not a pixel of a non-background color exists in a direction perpendicular to the arrangement direction at every position in the arrangement direction in the character string image, and recognizes the plurality of characters from the character regions and the additional character region.

Abstract: An object is to accurately identify a region corresponding to a position where an object is preset in an image. An object region identification device acquires a first frame image and a second frame image that are temporally successive. The object region identification device inputs the first frame image to a model that identifies an object in an image and acquires position information indicating a position in the first frame image, the position affecting identification of an object in the first frame image. The object region identification device acquires motion information indicating a motion of the object in the first frame image based on the first frame image and the second frame image. The object region identification device generates, based on the acquired position information and motion information, region information indicating a region in the first frame image, the region corresponding to a position where the object is present.