Abstract:
An action localization method, device, electronic equipment, and computer-readable storage medium are provided. The action localization method includes: identifying at least one target video segment containing a target object in a video; acquiring a first action recognition result of at least one image frame in the at least one target video segment and a second action recognition result of the target video segment; and acquiring an action localization result of the video based on the first action recognition result and the second action recognition result.
Abstract:
A method of and device for performing edit operations between devices is provided. The device includes a second device for receiving an operation object from a first device via a communication connection between the first device and the second device, and the second device performing an edit operation for the operation object, wherein the edit operation includes saving the operation object to a clipboard, and inputting the operation object into a current edit box.
Abstract:
The present disclosure provides methods, apparatuses, and computer-readable mediums for image processing. In some embodiments, a method of image processing includes acquiring, from a user, a first image. The method further includes removing, using an image de-filter network, a filter effect applied to the first image to generate a second image. The method further includes obtaining, based on the first image and the second image, an image filter corresponding to the filter effect. The method further includes rendering a third image using the obtained image filter to output a fourth image.
Abstract:
An electronic device and a method for controlling thereof are provided. A method for controlling an electronic device according to the disclosure includes obtaining a plurality of images for performing clustering, obtaining a plurality of target areas corresponding to each of the plurality of images, obtaining a plurality of feature vectors corresponding to the plurality of target areas, obtaining a plurality of central nodes corresponding to the plurality of feature vectors, obtaining neighbor nodes associated with each of the plurality of central nodes, obtaining a subgraph based on the plurality of central nodes and the neighbor nodes, identifying the connection probabilities between the plurality of central nodes of the subgraph and the neighbor nodes of each of the plurality of central nodes based on a graph convolutional network, and clustering the plurality of target areas based on the identified connection probabilities.