Abstract: An electronic device includes: a processor; and a memory storing instructions. By executing the instructions, the processor is configured to: receive a first image, recognize a plurality of objects in the first image to generate object information representing the plurality of objects, generate an object relationship graph including relationships between the plurality of objects, based on the first image and the object information, obtain image effect data including image effects to be respectively applied to the plurality of objects by inputting the object relationship graph to an image modification Graph Neural Network (GNN) model, and generate a modified image based on the first image, the object information, and the image effect data.

Abstract: An electronic device includes: a processor; and a memory storing instructions. By executing the instructions, the processor is configured to: receive a first image, recognize a plurality of objects in the first image to generate object information representing the plurality of objects, generate an object relationship graph including relationships between the plurality of objects, based on the first image and the object information, obtain image effect data including image effects to be respectively applied to the plurality of objects by inputting the object relationship graph to an image modification Graph Neural Network (GNN) model, and generate a modified image based on the first image, the object information, and the image effect data.

Abstract: An example method for controlling an electronic device includes detecting at least one user and acquiring user information of the detected at least one user; determining a user mode based on the acquired user information; determining a service to be provided to the detected at least one user, by inputting the user information and the determined user mode as input data to a model learned by an artificial intelligence algorithm; and providing the determined service corresponding to the user mode. A method for providing the service by the electronic device may at least partially use an artificial intelligence model learned according to at least one of machine learning, neural network, and deep learning algorithms.

Abstract: An electronic device for performing calibration is provided.

Abstract: An example method for controlling an electronic device includes detecting at least one user and acquiring user information of the detected at least one user; determining a user mode based on the acquired user information; determining a service to be provided to the detected at least one user, by inputting the user information and the determined user mode as input data to a model learned by an artificial intelligence algorithm; and providing the determined service corresponding to the user mode. A method for providing the service by the electronic device may at least partially use an artificial intelligence model learned according to at least one of machine learning, neural network, and deep learning algorithms.

Abstract: Provided are an artificial intelligence (AI) system that mimics cognitive functions, such as cognition and judgment, of the human brain using a machine learning algorithm such as deep learning and applications thereof. More particularly, provided is a device including a memory storing least one program and a first video, a display, and at least one processor configured to display the first video on at least one portion of the display by executing the at least one program, wherein the at least one program includes instructions for: comparing an aspect ratio of the first video with an aspect ratio of an area in which the first video is to be displayed, generating a second video corresponding to the aspect ratio of the area by using the first video when the aspect ratio of the first video is different from the aspect ratio of the area, and displaying the second video in the area, wherein the generating of the second video is performed by inputting at least one frame of the first video to an AI neural network.

Abstract: An electronic device includes: one or more processors; and memory, storing: a first training data set including pieces of 2D pose data and pieces of 3D pose data; and instructions that, when executed by the one or more processors, cause the electronic device to: train a neural network model to estimate 3D poses based on the first training data set; obtain an augmented data set by augmenting the first training data set; based on at least one of similarity or reliability of 3D pose augmented data in the augmented data set; select at least one piece of 3D pose augmented data among pieces of 3D pose augmented data in the augmented data set; obtain a second training data set including the 3D pose augmented data and 2D pose augmented data corresponding to the 3D pose augmented data; and retrain the neural network model based on the second training data set.

Abstract: Provided are an artificial intelligence (AI) system that mimics cognitive functions, such as cognition and judgment, of the human brain using a machine learning algorithm such as deep learning and applications thereof. More particularly, provided is a device including a memory storing least one program and a first video, a display, and at least one processor configured to display the first video on at least one portion of the display by executing the at least one program, wherein the at least one program includes instructions for: comparing an aspect ratio of the first video with an aspect ratio of an area in which the first video is to be displayed, generating a second video corresponding to the aspect ratio of the area by using the first video when the aspect ratio of the first video is different from the aspect ratio of the area, and displaying the second video in the area, wherein the generating of the second video is performed by inputting at least one frame of the first video to an AI neural network.

Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: Provided are a speech recognition device and method. The speech recognition device includes a microphone and a processor configured to receive a voice signal through the microphone, generate voice characteristic data by analyzing the voice signal by using a data recognition model based on a neural network, determine whether the voice signal is voice uttered from a user or voice output from an external device based on the voice characteristic data, and when the voice signal is determined as the voice uttered from the user, determine the voice signal as a voice command of the user and perform an operation corresponding to the voice command. The present disclosure also relates to an artificial intelligence (AI) system utilizing a machine learning algorithm such as deep learning, and applications thereof.

Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: An electronic apparatus is disclosed. The electronic apparatus includes a sensor, a camera, a memory, a camera and a processor. The memory stores a plurality of artificial intelligence models trained to identify objects and stores information on a map. The first processor provides, to the second processor, area information on an area in which the electronic apparatus is determined, based on sensing data obtained from the sensor, to be located, from among a plurality of areas included in the map. The second processor loads at least one artificial intelligence model of the plurality of artificial intelligence models to the volatile memory based on the area information and identifies an object by inputting the image obtained through the camera to the loaded artificial intelligence model.

Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: An electronic device and a method for controlling the same are disclosed. The method for controlling an electronic device according to the present disclosure comprises the steps of: detecting at least one user and acquiring user information of the detected at least one user; determining a user mode on the basis of the acquired user information; determining a service to be provided to the detected at least one user, by inputting the user information and the determined user mode as input data to a model learned by an artificial intelligence algorithm; and providing the determined service corresponding to the user mode. A method for providing the service by the electronic device may at least partially use an artificial intelligence model learned according to at least one of machine learning, neural network, and deep learning algorithms.

Abstract: Provided is a method, performed by an electronic device, of recognizing an object included in an image, the method including: extracting first object information from a first object included in a first image, obtaining a learning model for generating an image including a second object from the first object information, generating a second image including the second object by inputting the first object information to the learning model, comparing the first image with the second image, and recognizing the first object as the second object in the first image, based on a result of the comparing.

Abstract: A method of processing an image by an electronic device, includes: obtaining a label image of a label including at least one symbol associated with handling of clothing item; obtaining, from the label image, text information associated with the handling of the clothing item; identifying, from the label image, the at least one symbol associated with the handling of the clothing item, based on the text information and the label image; and obtaining handling information related to the clothing item based on the identified at least one symbol.

Abstract: An example method for controlling an electronic device includes detecting at least one user and acquiring user information of the detected at least one user; determining a user mode based on the acquired user information; determining a service to be provided to the detected at least one user, by inputting the user information and the determined user mode as input data to a model learned by an artificial intelligence algorithm; and providing the determined service corresponding to the user mode. A method for providing the service by the electronic device may at least partially use an artificial intelligence model learned according to at least one of machine learning, neural network, and deep learning algorithms.

Abstract: Provided is an image processing method according to an embodiment, which includes: obtaining a label of a first image by inputting the first image to a recognition model; obtaining reference style data for a target reference image to which a visual sentiment label is assigned, the visual sentiment label being the same as the obtained label from among visual sentiment labels pre-assigned to reference images; generating second style data based on first style data for the first image and the obtained reference style data; and generating a second image based on the generated second style data.

Abstract: A method performed by a server, may include: obtaining an image including a first text and a second text overlapping the first text; separating a first text region corresponding to the first text from the image; extracting pixels corresponding to the first text from the first text region to obtain an undamaged portion and a damaged portion of the first text; and reconstructing the first text by inpainting the damaged portion of the first text in which the first text overlaps the second text in the image.