A learning device includes a class classification learning unit that learns class classification of a classification target by using a loss function in which a loss is calculated to become smaller as a magnitude of a difference between a function value obtained by inputting a log-likelihood ratio to a function having a finite value range and a constant associated with a correct answer to the class classification of the classification target becomes smaller, the log-likelihood ratio being the logarithm of a ratio between the likelihood that the classification target belongs to a first class and the likelihood that the classification target belongs to a second class.

An image recognition method and apparatus, a training method, an electronic device, and a storage medium are provided. The image recognition method includes: acquiring an image to be recognized, the image to be recognized including a target text; and determining text content of the target text based on knowledge information and image information of the image to be recognized.

An image recognition method and apparatus, a training method, an electronic device, and a storage medium are provided. The image recognition method includes: acquiring an image to be recognized, the image to be recognized including a target text; and determining text content of the target text based on knowledge information and image information of the image to be recognized.

Non-limiting embodiments of the present technology are directed to a method and system for generating a training dataset. The method comprises: accessing data associated with a plurality of assessors executing digital tasks of a first type and digital tasks of a second type; generating, a first ranked list of assessors and a second ranked list of assessors based on their past performance; for a given one of the plurality of assessors: generating, a class score for the common class of digital tasks; acquiring a request for executing a digital task of a third type; ranking, the plurality of assessors based on respective class scores, the given one from the plurality of assessors being one of top ranked ones from the plurality of assessors; transmitting the digital task of the third type to the given one; generating the training data for the MLA based on a response from the given one.

The objective of the present invention is to provide an image classification device and a method therefor with which suitable teaching data can be created. An image classification device that carries out image classification using images which are in a class to be classified and include teaching information, and images which are in a class not to be classified and to which teaching information has not been assigned, said image classification device being characterized by being provided with: an image group input unit for receiving inputs of an image group belonging to a class to be classified and an image group belonging to a class not to be classified; and a subclassification unit for extracting a feature amount for each image in an image group, clustering the feature amounts of the images in the image group belonging to a class not to be classified, and thereby dividing the images into sub-classes.

A computer implemented method of anonymising video surveillance data of a scene and detecting an object or event of interest in such anonymised video surveillance data, the method comprising segmenting frames of video surveillance data of at least one scene into corresponding frames of segmented data using image segmentation, wherein a mask label is assigned to every pixel of each frame of the segmented data based either on a class of objects or of surfaces or on an instance of such a class that pixel belongs to, and detecting at least one object and/or event of interest based on at least one shape and/or motion in at least one frame of the segmented data.

A method for displaying images similar to a selected image includes receiving, from a user, a selection of an anchor image, generating, using a machine learning model, an anchor embeddings set for the anchor image and respective candidate embeddings sets for a plurality of candidate images. The method also includes calculating a distance between the anchor embeddings set and each of the plurality of candidate embeddings sets and displaying at least one of the plurality of candidate images based on the calculated distance.

Teacher and student models output inference results for training data. A loss calculation unit calculates a total loss using at least one of (1) a loss obtained by multiplying a difference between a true value and a student model output by a weight increasing as a confidence of the teacher model output is lower, (2) a loss obtained by multiplying a difference between the true value and the student model output by a weight increasing as a difference between the true value and the teacher model output is greater, and (3) a loss obtained by multiplying a difference between the teacher and student model outputs by weights increasing as the difference between the teacher and student model outputs is greater and increasing as the difference between the true value and the teacher model output is smaller. An update part updates parameters of the student model based on the total loss.

A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.