A non-transitory computer-readable storage medium storing a determination program that causes at least one computer to execute a process, the process includes acquiring a group of captured images that includes images including a face to which markers are attached; selecting, from a plurality of patterns that indicates a transition of positions of the markers, a first pattern that corresponds to a time-series change in the positions of the markers included in consecutive images among the group of captured images; and determining occurrence intensity of an action based on a determination criterion of the action determined based on the first pattern and the positions of the markers included in a captured image included after the consecutive images among the group of captured images.

Disclosed are a method for training a Student Network and a method for recognizing an image. The method includes: acquiring first prediction feature information of a sample image on the first granularity and second prediction feature information of the sample image on the second granularity by inputting the sample image into a Student Network, and acquiring first feature information of the sample image on the first granularity and second feature information of the sample image on the second granularity by inputting the sample image into a Teacher Network, and acquiring a target Student Network.

Disclosed are a method for training a Student Network and a method for recognizing an image. The method includes: acquiring first prediction feature information of a sample image on the first granularity and second prediction feature information of the sample image on the second granularity by inputting the sample image into a Student Network, and acquiring first feature information of the sample image on the first granularity and second feature information of the sample image on the second granularity by inputting the sample image into a Teacher Network, and acquiring a target Student Network.

An image-based animal object condition identification apparatus includes: a communication module that receives an image of an object; a memory that stores therein a program configured to extract animal condition information from the received image; and a processor that executes the program. The program extracts continuous animal detection information of each object by inputting the received image into an animal detection model that is trained based on learning data composed of animal images and determines predetermined animal condition information for each class of each animal object by inputting the continuous animal detection information of each object into an animal condition identification model.

An image-based animal object condition identification apparatus includes: a communication module that receives an image of an object; a memory that stores therein a program configured to extract animal condition information from the received image; and a processor that executes the program. The program extracts continuous animal detection information of each object by inputting the received image into an animal detection model that is trained based on learning data composed of animal images and determines predetermined animal condition information for each class of each animal object by inputting the continuous animal detection information of each object into an animal condition identification model.

A method for parallel processing a digitally scanned pathology image is performed by a plurality of processors and includes performing, by a first processor, a first operation of generating a first batch from a first set of patches extracted from a digitally scanned pathology image and providing the generated first batch to a second processor, performing, by the first processor, a second operation of generating a second batch from a second set of patches extracted from the digitally scanned pathology image and providing the generated second batch to the second processor, and performing, by the second processor, a third operation of outputting a first analysis result from the first batch by using a machine learning model, with at least part of time frame for the second operation performed by the first processor overlapping at least part of time frame for the third operation performed by the second processor.

A method for parallel processing a digitally scanned pathology image is performed by a plurality of processors and includes performing, by a first processor, a first operation of generating a first batch from a first set of patches extracted from a digitally scanned pathology image and providing the generated first batch to a second processor, performing, by the first processor, a second operation of generating a second batch from a second set of patches extracted from the digitally scanned pathology image and providing the generated second batch to the second processor, and performing, by the second processor, a third operation of outputting a first analysis result from the first batch by using a machine learning model, with at least part of time frame for the second operation performed by the first processor overlapping at least part of time frame for the third operation performed by the second processor.

A system for generating three-dimensional (3D) objects from two-dimensional (2D) images of garments is presented. The system includes a data module configured to receive a 2D image of a selected garment and a target 3D model. The system further includes a computer vision model configured to generate a UV map of the 2D image of the selected garment. The system moreover includes a training module configured to train the computer vision model based on a plurality of 2D training images and a plurality of ground truth (GT) panels for a plurality of 3D training models. The system furthermore includes a 3D object generator configured to generate a 3D object corresponding to the selected garment based on the UV map generated by a trained computer vision model and the target 3D model. A related method is also presented.

A system for generating three-dimensional (3D) objects from two-dimensional (2D) images of garments is presented. The system includes a data module configured to receive a 2D image of a selected garment and a target 3D model. The system further includes a computer vision model configured to generate a UV map of the 2D image of the selected garment. The system moreover includes a training module configured to train the computer vision model based on a plurality of 2D training images and a plurality of ground truth (GT) panels for a plurality of 3D training models. The system furthermore includes a 3D object generator configured to generate a 3D object corresponding to the selected garment based on the UV map generated by a trained computer vision model and the target 3D model. A related method is also presented.

Systems, methods, and computer readable media for messaging system with augmented reality (AR) makeup are presented. Methods include processing a first image to extract a makeup portion of the first image, the makeup portion representing the makeup from the first image and training a neural network to process images of people to add AR makeup representing the makeup from the first image. The methods may further include receiving, via a messaging application implemented by one or more processors of a user device, input that indicates a selection to add the AR makeup to a second image of a second person. The methods may further include processing the second image with the neural network to add the AR makeup to the second image and causing the second image with the AR makeup to be displayed on a display device of the user device.