Abstract: A processor-implemented method with neural network training includes: determining first backbone feature data corresponding to each input data by applying, to a first neural network model, two or more sets of the input data of the same scene, respectively; determining second backbone feature data corresponding to each input data by applying, to a second neural network model, the two or more sets of the input data, respectively; determining projection-based first embedded data and dropout-based first view data from the first backbone feature data; and determining projection-based second embedded data and dropout-based second view data from the second backbone feature data; and training either one or both of the first neural network model and the second neural network model based on a loss determined based on a combination of any two or more of the first embedded data, the first view data, the second embedded data, the second view data, and an embedded data clustering result.

Abstract: A method of generating a model for image processing includes increasing, using a receptive field (RF) increasing module of at least one processor, a receptive field of an input image frame; generating, using a feature extraction (FE) module of the at least one processor, a feature map based on the input image frame with an increased receptive field; generating, using a super resolution (SR) module of the at least one processor, a target image having a target resolution, based on the feature map; and generating, using a model generation (MG) module of the at least one processor, a replacement model that replaces at least one of the RF increasing module, the FE module, and the SR module.

Abstract: A processor-implemented method includes: initializing a neural network model with arbitrary values using a random seed; training the neural network model based on the arbitrary values; determining a number of coats and respective densities of the coats; learning respective scores of parameters of the neural network model based on the number of coats and the respective densities of the coats; determining mask information for determining the parameters of the neural network model to be comprised in each of the coats based on the scores; and generating a bitstream based on the number of coats, the respective densities of the coats, the mask information, and the random seed.

Abstract: An object detection training method and apparatus are provided. The object detection training apparatus determines a pose and a dimension of an object, and a bounding box at various viewpoints from an input image based on an object detection model, and trains the object detection model based on a loss.

Abstract: A processor-implemented method with neural network training includes: determining first backbone feature data corresponding to each input data by applying, to a first neural network model, two or more sets of the input data of the same scene, respectively; determining second backbone feature data corresponding to each input data by applying, to a second neural network model, the two or more sets of the input data, respectively; determining projection-based first embedded data and dropout-based first view data from the first backbone feature data; and determining projection-based second embedded data and dropout-based second view data from the second backbone feature data; and training either one or both of the first neural network model and the second neural network model based on a loss determined based on a combination of any two or more of the first embedded data, the first view data, the second embedded data, the second view data, and an embedded data clustering result.

Abstract: A method of detecting a 3D object from a 2D image includes receiving a 2D image including an object, acquiring an object detection region from the 2D image, iteratively searching for candidates for a direction of a volume including the object of the 2D image in a 3D coordinate system based on the object detection region, and detecting the volume from the 3D coordinate system based on results of the iterative searching.

Abstract: There is provided an application stored in a computer-readable storage medium for a first terminal to perform a method of providing a video call service, the method including: establishing a video call session between the first terminal of a first user and a second terminal of a second user; preparing facial expression information of the second user accumulated in at least one video call session executed by the second terminal before the video call session; determining emotion information corresponding to the facial expression information of the second user based on the facial expression information of the second user; and providing the determined emotion information through an execution screen of the first terminal.

Abstract: Provided is an application stored in a computer-readable storage medium to perform a method of providing a video call service using icon information corresponding to a facial expression of a user.

Abstract: An image recognition method using a region-based convolutional neural network (R-CNN) includes generating a feature map from an input image, detecting one or more regions of interest (ROIs) in the feature map, classifying the ROIs into groups based on setting information, performing pooling on the ROIs classified into the groups independently for each of the groups, and performing a regression operation on a result of the pooling and applying an image classifier to a result of the regression operation.

Abstract: An autonomous vehicle and method of providing driving information of an autonomous vehicle, the method includes acquiring outside situation data via a sensor in the autonomous vehicle, generating, based on the acquired outside situation data, a local map comprising the autonomous vehicle, one or more external vehicles within a threshold distance from the autonomous vehicle, and a road on which the autonomous vehicle is travelling, and displaying the generated local map on a display of the autonomous vehicle.

Abstract: Provided is an application stored in a computer-readable storage medium to perform a method of providing a video call service using icon information corresponding to a facial expression of a user.

Abstract: A method of detecting a 3D object from a 2D image includes receiving a 2D image including an object, acquiring an object detection region from the 2D image, iteratively searching for candidates for a direction of a volume including the object of the 2D image in a 3D coordinate system based on the object detection region, and detecting the volume from the 3D coordinate system based on results of the iterative searching.

Abstract: An object detection training method and apparatus are provided. The object detection training apparatus determines a pose and a dimension of an object, and a bounding box at various viewpoints from an input image based on an object detection model, and trains the object detection model based on a loss.

Abstract: Provided is an application stored in a computer-readable storage medium to perform a method of providing a video call service using icon information corresponding to a facial expression of a user.

Abstract: Provided is an application stored in a computer-readable storage medium to perform a method of providing a video call service using icon information corresponding to a facial expression of a user.

Abstract: An image recognition method using a region-based convolutional neural network (R-CNN) includes generating a feature map from an input image, detecting one or more regions of interest (ROIs) in the feature map, classifying the ROIs into groups based on setting information, performing pooling on the ROIs classified into the groups independently for each of the groups, and performing a regression operation on a result of the pooling and applying an image classifier to a result of the regression operation.

Abstract: An autonomous vehicle and method of providing driving information of an autonomous vehicle, the method includes acquiring outside situation data via a sensor in the autonomous vehicle, generating, based on the acquired outside situation data, a local map comprising the autonomous vehicle, one or more external vehicles within a threshold distance from the autonomous vehicle, and a road on which the autonomous vehicle is travelling, and displaying the generated local map on a display of the autonomous vehicle.

Abstract: There is provided an application stored in a computer-readable storage medium for a first terminal to perform a method of providing a video call service, the method including: establishing a video call session between the first terminal of a first user and a second terminal of a second user; preparing facial expression information of the second user accumulated in at least one video call session executed by the second terminal before the video call session; determining emotion information corresponding to the facial expression information of the second user based on the facial expression information of the second user; and providing the determined emotion information through an execution screen of the first terminal.