Abstract: A liquid crystal panel and a manufacturing method thereof, and a display apparatus are disclosed. The liquid crystal panel includes a liquid crystal cell, a first adhesive layer, a first polarizer, a second adhesive layer and an optical composite film; the first adhesive layer is located on a first side of the liquid crystal cell; the first polarizer is located on a side of the first adhesive layer away from the liquid crystal cell; the second adhesive layer is located on a side of the first polarizer away from the first adhesive layer; the optical composite film is located on a side of the second adhesive layer away from the first polarizer; the first adhesive layer adheres the first polarizer onto the liquid crystal cell, the second adhesive layer adheres the optical composite film onto the first polarizer, and the optical composite film includes diffusion particles.

Abstract: Embodiments of the present disclosure include obtaining a text prompt describing an element, layout information indicating a target region for the element, and a precision level corresponding to the element. Some embodiments generate a text feature pyramid based on the text prompt, the layout information, and the precision level, wherein the text feature pyramid comprises a plurality of text feature maps at a plurality of scales, respectively. Then, an image is generated based on the text feature pyramid. In some cases, the image includes an object corresponding to the element of the text prompt at the target region. Additionally, a shape of the object corresponds to a shape of the target region based on the precision level.

Abstract: Embodiments of this application provide a face recognition method and apparatus, including: obtaining TOF data; processing the TOF data into a TOF image in a TEE; and performing face recognition by using the TOF image, to obtain a recognition result. Because the TOF data is used for face recognition, higher security is achieved. Implementation in the TEE can further improve the security.

Abstract: Provided is a display panel. The display panel includes: a driving backplane, including a base substrate; a light-emitting device on a side of the driving backplane; a package structure on a side, facing away from the driving backplane, of the light-emitting device, wherein the package structure includes an inorganic package structure and an organic package structure, the inorganic package structure being closer to the driving backplane than the organic package structure; and a color stop layer on a side, facing away from the driving backplane, of the organic package structure, wherein the color stop layer is attached to the inorganic package structure, and in a direction perpendicular to the base substrate, a maximum thickness of the organic package structure is less than a thickness of the inorganic package structure.

Abstract: An electrolyte including one or more nitrile benzoquinone compounds, and the nitrile benzoquinone compound is selected from the group consisting of the compounds represented by formula I, formula II, and formula III: The substituents R1 to R9 are each independently selected from the group consisting of hydrogen, a C2 to C12 ether group, a C1 to C12 alkoxy group, halogen, a C1 to C12 alkyl group, a C2 to C12 alkenyl group, a C2 to C12 alkynyl group, and a C6 to C26 aryl group. The electrolyte can form a stable protective film on a cathode, thereby increasing the cycle capacity retention rate and high temperature storage performance of an electrochemical device.

Abstract: Systems and methods for image dense field based view calibration are provided. In one embodiment, an input image is applied to a dense field machine learning model that generates a vertical vector dense field (VVF) and a latitude dense field (LDF) from the input image. The VVF comprises a vertical vector of a projected vanishing point direction for each of the pixels of the input image. The latitude dense field (LDF) comprises a projected latitude value for the pixels of the input image. A dense field map for the input image comprising the VVF and the LDF can be directly or indirectly used for a variety of image processing manipulations. The VVF and LDF can be optionally used to derive traditional camera calibration parameters from uncontrolled images that have undergone undocumented or unknown manipulations.

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: 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: A unidirectional conductive joint assembly includes a unidirectional conductive device, wherein a conductive sheet is electrically connected to a conductive end of the unidirectional conductive device, another conductive sheet is electrically connected to another conductive end of the unidirectional conductive device, insulating films are laminated on two sides of each conductive sheet, and each conductive sheet is sandwiched between two insulating films; and the two conductive ends of the unidirectional conductive device are insulated and sealed in an insulating package, portions of the conductive sheets on the sides of the insulating films close to the unidirectional conductive device are first conductive portions, the first conductive portions and film sides of the insulating films close to the first conductive portions are all insulated and sealed in the insulating package, and the conductive sheets and the insulating films are all partially located outside the insulating package.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a generative inpainting neural network to accurately generate inpainted digital images via object-aware training and/or masked regularization. For example, the disclosed systems utilize an object-aware training technique to learn parameters for a generative inpainting neural network based on masking individual object instances depicted within sample digital images of a training dataset. In some embodiments, the disclosed systems also (or alternatively) utilize a masked regularization technique as part of training to prevent overfitting by penalizing a discriminator neural network utilizing a regularization term that is based on an object mask. In certain cases, the disclosed systems further generate an inpainted digital image utilizing a trained generative inpainting model with parameters learned via the object-aware training and/or the masked regularization.

Abstract: Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.

Abstract: Systems and methods for image generation are described. Embodiments of the present disclosure receive a text phrase that describes a target image to be generated; generate text features based on the text phrase; retrieve a search image based on the text phrase; and generate the target image using an image generation network based on the text features and the search image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and/or implementing machine learning models utilizing compressed log scene measurement maps. For example, the disclosed system generates compressed log scene measurement maps by converting scene measurement maps to compressed log scene measurement maps by applying a logarithmic function. In particular, the disclosed system uses scene measurement distribution metrics from a digital image to determine a base for the logarithmic function. In this way, the compressed log scene measurement maps normalize ranges within a digital image and accurately differentiates between scene elements objects at a variety of depths. Moreover, for training, the disclosed system generates a predicted scene measurement map via a machine learning model and compares the predicted scene measurement map with a compressed log ground truth map.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a text prompt. A text encoder encodes the text prompt to obtain a preliminary text embedding. An adaptor network generates an adapted text embedding based on the preliminary text embedding. In some cases, the adaptor network is trained to adapt the preliminary text embedding for generating an input to an image generation model. The image generation model generates a synthetic image based on the adapted text embedding. In some cases, the synthetic image includes content described by the text prompt.

Abstract: The present disclosure relates to the technical field of wearable devices. Provided are a hearing aid control method and apparatus, a hearing aid device, and a storage medium. The hearing aid control method includes: displaying a hearing detection image in a viewing window area of the pair of augmented reality (AR) glasses, playing hearing test audio to acquire a feedback signal from a user wearing the hearing aid device based on the hearing test audio, and determining a hearing assessment result for the user based on the feedback signal; in response to determining, based on the hearing assessment result, that a display operation is required to be performed by the hearing aid device, collecting speech information; and converting the speech information into text information upon the collection of the speech information, and displaying the text information in the viewing window area of the pair of AR glasses.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a height map for a digital object portrayed in a digital image and further utilizes the height map to generate a shadow for the digital object. Indeed, in one or more embodiments, the disclosed systems generate (e.g., utilizing a neural network) a height map that indicates the pixels heights for pixels of a digital object portrayed in a digital image. The disclosed systems utilize the pixel heights, along with lighting information for the digital image, to determine how the pixels of the digital image project to create a shadow for the digital object. Further, in some implementations, the disclosed systems utilize the determined shadow projections to generate (e.g., utilizing another neural network) a soft shadow for the digital object. Accordingly, in some cases, the disclosed systems modify the digital image to include the shadow.

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: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a dual-branched neural network architecture to harmonize composite images. For example, in one or more implementations, the transformer-based harmonization system uses a convolutional branch and a transformer branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image.