An electronic device and a controlling method thereof are provided. A controlling method of an electronic device according to the disclosure includes: performing first learning for a neural network model for acquiring a video sequence including a talking head of a random user based on a plurality of learning video sequences including talking heads of a plurality of users, performing second learning for fine-tuning the neural network model based on at least one image including a talking head of a first user different from the plurality of users and first landmark information included in the at least one image, and acquiring a first video sequence including the talking head of the first user based on the at least one image and pre-stored second landmark information using the neural network model for which the first learning and the second learning were performed.

A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having an estimated cellularity of 0%, or suspect, having a cellularity roughly estimated to be greater than 0%; operating on each suspect patch using a second DCNN, trained using a deep ordinal regression model, to determine an estimated cellularity score for that suspect patch; and combining the estimated cellularity scores of the patches of interest to provide an estimated cellularity for the digital pathology slide image at a patch-by-patch level.

Provided are an ultrasound image detection system and a method thereof based on artificial intelligence (AI) automatic labeling of anatomical structures, including: a receiving module, an image recognition module having an object detection model, an image processing module and a display module, wherein the image recognition module utilizes the object detection model to perform object detection on the image to be recognized, which is received by the receiving module, and then obtains a plurality of object recognition images with object detection results. Then, the image processing module detects missed anatomical structures according to the object detection results of the plurality of object recognition images, thereby outputting an object detection image. Additionally, the display module displays the object detection image. Therefore, the anatomical structures in the ultrasound image can be automatically and instantly recognized by AI so as to provide accurate judgment basis for medical personnel.

Disclosed herein are a learning-based three-dimensional (3D) model creation apparatus and method. A method for operating a learning-based 3D model creation apparatus includes generating multi-view feature images using supervised learning, creating a three-dimensional (3D) mesh model using a point cloud corresponding to the multi-view feature images and a feature image representing internal shape information, generating a texture map by projecting the 3D mesh model into three viewpoint images that are input, and creating a 3D model using the texture map.

A semantic image segmentation (SIS) system includes: a semantic segmentation module trained to segment objects belonging to predetermined classes in input images using training images; and a learning module configured to selectively update at least one parameter of each of a localizer module, an encoder module, and a decoder module of the semantic segmentation module to identify objects having a new class that is not one of the predetermined classes: based on an image level class for a learning image including an object having the new class that is not one of the predetermined classes; and without a pixel-level annotation for the learning image.

In various examples, systems and methods are disclosed relating to generating a response from image and/or video input for image/video-based artificial intelligence (AI) systems and applications. Systems and methods are disclosed for a first model (e.g., a teacher model) distilling its knowledge to a second model (a student model). The second model receives a downstream image in a downstream task and generates at least one feature. The first model generates first features corresponding to an image which can be a real image or a synthetic image. The second model generates second features using the image as an input to the second model. Loss with respect to first features is determined. The second model is updated using the loss.

An image processing method includes acquiring consecutive time-series images captured by an onboard camera and including at least one image having a first annotation indicating a first region; determining, for each of the images, in reverse chronological order from an image of the last time point, whether the first region exists in the image based on whether the first annotation is attached; identifying the first image of a first time point for which the first region is determined not to exist, and setting a second region including a partial region of an object in the identified first image, indicating the moving object that is obstructed by the object before appearing on the path, and having dimensions based on dimensions of the first region in an image of a second time point immediately after the first time point; and attaching a second annotation to the image corresponding to the second time point, the second annotation indicating the second region.

Systems and methods are provided for image enhancement using self-examples in combination with external examples. In one embodiment, an image manipulation application receives an input image patch of an input image. The image manipulation application determines a first weight for an enhancement operation using self-examples and a second weight for an enhancement operation using external examples. The image manipulation application generates a first interim output image patch by applying the enhancement operation using self-examples to the input image patch and a second interim output image patch by applying the enhancement operation using external examples to the input image patch. The image manipulation application generates an output image patch by combining the first and second interim output image patches as modified using the first and second weights.

A computing device automatically classifies an observation vector. (a) A converged classification matrix is computed that defines a label probability for each observation vector. (b) The value of the target variable associated with a maximum label probability value is selected for each observation vector. Each observation vector is assigned to a cluster. A distance value is computed between observation vectors assigned to the same cluster. An average distance value is computed for each observation vector. A predefined number of observation vectors are selected that have minimum values for the average distance value. The supervised data is updated to include the selected observation vectors with the value of the target variable selected in (b). The selected observation vectors are removed from the unlabeled subset. (a) and (b) are repeated. The value of the target variable for each observation vector is output to a labeled dataset.

A recommendation method includes retrieving content consumption data including content consumed and content not consumed. Based on the content consumption data, identifying a first piece of content not consumed. A first feature of the first piece of content related to negative consumption of the first piece of content is determined. A first system is used to revise the first feature to a second feature. A second piece of content including the second feature is provided to an electronic device. The second piece of content is a revised instance of the first piece of content.