Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that perform language guided digital image editing utilizing a cycle-augmentation generative-adversarial neural network (CAGAN) that is augmented using a cross-modal cyclic mechanism. For example, the disclosed systems generate an editing description network that generates language embeddings which represent image transformations applied between a digital image and a modified digital image. The disclosed systems can further train a GAN to generate modified images by providing an input image and natural language embeddings generated by the editing description network (representing various modifications to the digital image from a ground truth modified image). In some instances, the disclosed systems also utilize an image request attention approach with the GAN to generate images that include adaptive edits in different spatial locations of the image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for image generation are described. An embodiment of the present disclosure includes obtaining an input image, an inpainting mask, and a plurality of content preservation values corresponding to different regions of the inpainting mask, and identifying a plurality of mask bands of the inpainting mask based on the plurality of content preservation values. An image generation model generates an output image based on the input image and the inpainting mask. The output image is generated in a plurality of phases. Each of the plurality of phases uses a corresponding mask band of the plurality of mask bands as an input.

Abstract: Some embodiments of the present disclosure provide a DRX parameter configuration method, a DRX processing method, a network node, and a user equipment. The DRX parameter configuration method is applied in the network node and includes: determining (101) one or more service types corresponding to a User Equipment (UE) based on the characteristic information of the UE; configuring (102) a DRX parameter for each of the one or more service types; and transmitting (103) the DRX parameter to the UE. With the embodiments of the present disclosure, requirements of data transmission under different application scenes can be met.

Abstract: Systems, methods, and non-transitory computer-readable media implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, one or more embodiments involve receiving an input digital image and search input and further modifying the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and one or more embodiments involve retrieving the image search results utilizing a weighted combination of the queries. Some implementations involve generating an input embedding for the search input (e.g., the multi-modal search input) and retrieving the image search results using the input embedding.

Abstract: Techniques for performing a digital operation on a digital image are described along with methods and systems employing such techniques. According to the techniques, an input (e.g., an input stroke) is received by, for example, a processing system. Based upon the input, an area of the digital image upon which a digital operation (e.g., for removal of a distractor within the area) is to be performed is determined. In an implementation, one or more metrics of an input stroke are analyzed, typically in real time, to at least partially determine the area upon which the digital operation is to be performed. In an additional or alternative implementation, the input includes a first point, a second point and a connector, and the area is at least partially determined by a location of the first point relative to a location of the second point and/or by locations of the first point and/or second point relative to one or more edges of the digital image.

Abstract: A time-delay compensation method and apparatus, and a device. The method comprises: a time-delay compensation method, which is applied to a terminal device. The time-delay compensation method comprises: receiving first information, the first information being used for the terminal device to determine whether to perform a transmission time-delay compensation; and determining whether to perform the transmission time-delay compensation according to the first information. In the present application, the terminal device determines, according to the first information sent by a network device, whether a transmission time-delay compensation is required, so as to solve the problem in the prior art that when transmission time-delay compensation is performed is unknown, so that service transmission in a communications network can achieve accuracy requirements of time synchronization.

Abstract: Various disclosed embodiments are directed to resizing, via down-sampling and up-sampling, a high-resolution input image in order to meet machine learning model low-resolution processing requirements, while also producing a high-resolution output image for image inpainting via a machine learning model. Some embodiments use a refinement model to refine the low-resolution inpainting result from the machine learning model such that there will be clear content with high resolution both inside and outside of the mask region in the output. Some embodiments employ new model architecture for the machine learning model that produces the inpainting result—an advanced Cascaded Modulated Generative Adversarial Network (CM-GAN) that includes Fast Fourier Convolution (FCC) layers at the skip connections between the encoder and decoder.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.

Abstract: A decoder for decoding a codeword of a Tunstall code is provided, including: a sub-decoder configured to receive an input codeword of the Tunstall code to the decoder and output a decoded symbol of the input codeword: a symbol memory configured to receive and store the decoded symbol of the input codeword from the sub-decoder; and a controller configured to control the symbol memory to output one or more decoded symbols stored in the symbol memory.

Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.

Abstract: Systems and methods provide editing operations in a smart editing system that may generate a focal point within a mask of an object for each frame of a video segment and perform editing effects on the frames of the video segment to quickly provide users with natural video editing effects. An eye-gaze network may produce a hotspot map of predicted focal points in a video frame. These predicted focal points may then be used by a gaze-to-mask network to determine objects in the image and generate an object mask for each of the detected objects. This process may then be repeated to effectively track the trajectory of objects and object focal points in videos. Based on the determined trajectory of an object in a video clip and editing parameters, the editing engine may produce editing effects relative to an object for the video clip.

Abstract: An epitaxial structure includes a substrate, a buffer layer, a channel layer, a barrier layer, a diffusion barrier layer, and a P-type gallium nitride layer sequentially stacked from bottom to top. The P-type gallium nitride layer has a first lattice constant. The diffusion barrier layer includes a chemical composition of Inx1Aly1Gaz1N, where x1+y1+z1=1, 0?x1?0.3, 0?y1?1.0, and 0?z1?1.0. The chemical composition of the diffusion barrier layer has a proportional relationship so that the diffusion barrier layer has a second lattice constant that matches the first lattice constant, and the second lattice constant is between 80% and 120% of the first lattice constant.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that trains (and utilizes) an image color editing diffusion neural network to generate a color edited digital image(s) for a digital image. In particular, in one or more implementations, the disclosed systems identify a digital image depicting content in a first color style. Moreover, the disclosed systems generate, from the digital image utilizing an image color editing diffusion neural network, a color-edited digital image depicting the content in a second color style different from the first color style. Further, the disclosed systems provide, for display within a graphical user interface, the color-edited digital image.

Abstract: A novel rheology modifier which comprises a quaternary ammonium containing polyamide for use in aqueous paint, and that can provide excellent pigment suspension and rheological properties to the aqueous based coating without being affected by pH fluctuation.

Abstract: Systems and methods for image processing are configured. Embodiments of the present disclosure encode a content image and a style image using a machine learning model to obtain content features and style features, wherein the content image includes a first object having a first appearance attribute and the style image includes a second object having a second appearance attribute; align the content features and the style features to obtain a sparse correspondence map that indicates a correspondence between a sparse set of pixels of the content image and corresponding pixels of the style image; and generate a hybrid image based on the sparse correspondence map, wherein the hybrid image depicts the first object having the second appearance attribute.

Abstract: This application relates to a data transmission method and apparatus, a computer device, and a storage medium. A first device (01) sends indication information to a second device (02), so that the first device (01) informs the second device (02) of a usage status of a target resource in time, and after receiving the indication information, the second device (02) may determine, based on the indication information, which resources to use, which resources to receive or decode, which resources not to receive or decode, which resources to terminate, and the like.

Abstract: Models for classifying exposure defects in images are provided by training a binary model on a dataset of images labeled to indicate exposure within the images. When trained, the binary model classifies an image based on whether the image includes an exposure defect. A classification model is also trained. The classification model is trained on a dataset of images having exposure defects labeled to indicate exposure scores or exposure defect classifications. When trained, the classification model classifies the image based on a level of exposure. The binary model and the classification model can be stored for identifying and classifying exposure defects within images.

Abstract: The application discloses an NFC antenna for handheld devices and a handheld device. The NFC antenna includes an antenna base and a coupling coil arranged along the edge of the antenna base. The antenna base integrally includes a first antenna base and two second antenna bases. The first antenna base is arranged on a backside of the handheld device, with two sides bending and extending towards the frontside of the handheld device to form the second antenna bases. The second antenna bases are respectively located on sides of the handheld device and outside the screen assembly of the handheld device. The NFC antenna covers the sides of the handheld device, so that NFC antenna signals at the sides can be sensed at the frontside, thereby NFC cards can be swiped at both the frontside and backside of the handheld device, without occupying space at the frontside.