Abstract: An image processing method including obtaining a fake template sample group comprising a first source image, a real labeled image, and a fake template image, inputting the fake template image into an identity swapping model to obtain a first identity swapping image of the fake template image, obtaining a fake labeled sample group comprising a second source image, a real template image, and a fake labeled image, the fake labeled image being based on identity swapping processing of the real template image, inputting the real template image into the identity swapping model to obtain a second identity swapping image of the real template image, and training the identity swapping model based on the fake template sample group, the first identity swapping image, the fake labeled sample group, and the second identity swapping image to generate a trained identity swapping model.

Abstract: In an image optimization method, an image generation network, a to-be-optimized image, and a plurality of preset image features are obtained. A target feature is selected from the plurality of preset image features based on (i) the target feature and the to-be-optimized image and (ii) a preset similarity condition. The target feature and an initial offset parameter are input to the image generation network. The initial offset parameter is adjusted according to a difference between an output of the image generation network and the to-be-optimized image, to obtain a target offset parameter. The target feature and the target offset parameter are input to the image generation network, to generate an optimized image. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Abstract: The present disclosure discloses an encapsulation structure, an encapsulation method and a display apparatus. The encapsulation structure includes at least two encapsulation structure layers that cover an outer side of a device to be encapsulated, at least one of the at least two encapsulation structure layers includes an inorganic layer and an organic layer that are superimposed. The inorganic layer comprises a first sub-inorganic layer and a second sub-inorganic layer that are superimposed, a first opening is in the first sub-inorganic layer, a second opening is in the second sub-inorganic layer, and an elastic structure is disposed in each of the first opening and the second opening.

Abstract: This application discloses a method for generating an image processing model performed by a computer device. The method includes: performing training by using a first source image sample, a first template image sample, and a first standard synthesized image, to obtain a first parameter adjustment model, and combining the first parameter adjustment model and a first resolution update layer into a first update model; adjusting the first update model into a second parameter adjustment model using a second source image sample and a second template image sample and a second standard synthesized image; combining the second parameter adjustment model and a second resolution update layer into a second update model; and adjusting the second update model into a target image fusion model using a third source image sample, a third template image sample and a third standard synthesized image.

Abstract: A target tracking method is provided for a computer device. The method includes determining a target candidate region of a current image frame; capturing a target candidate image matching the target candidate region from the current image frame; determining a target region of the current image frame according to an image feature of the target candidate image; determining motion prediction data of a next image frame relative to the current image frame by using a motion prediction model and according to the image feature of the target candidate image; and determining a target candidate region of the next image frame according to the target region and the motion prediction data.

Abstract: An action recognition method includes: obtaining original feature submaps of each of temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, by using each of the temporal frames as a target temporal frame, motion information weights of the target temporal frame on the convolutional channels according to original feature submaps of the target temporal frame and original feature submaps of a next temporal frame, and obtaining motion information feature maps of the target temporal frame on the convolutional channels according to the motion information weights; performing temporal convolution on the motion information feature maps of the target temporal frame to obtain temporal motion feature maps of the target temporal frame; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps of the target temporal frame on the convolutional channels.

Abstract: Provided by a bandpass filter circuit and a multiplexer. The bandpass filter circuit includes at least one electromagnetic LC filter circuit and at least one acoustic wave resonance unit. The at least one acoustic wave resonance unit includes an input port, an output port, at least one circuit element and at least three resonators. The at least one electromagnetic LC filter circuit is electrically connected to the at least one acoustic wave resonance unit, and the at least three resonators include at least one first resonator and at least one second resonator. In a case where the at least one first resonator includes one first resonator, the first resonator is connected in series between the input port and the output port.

Abstract: This application relates to a method and an apparatus for detecting facial key points, a computer device, and a storage medium including: acquiring a to-be-detected face image from a current frame; determining partial images in the to-be-detected face image, each partial image including one or more facial key points; determining, within each of the partial images, candidate points of the one or more facial key points corresponding to the partial image, respectively; and jointly constraining the candidate points in the partial images to determine a set of facial key points from the candidate points for the to-be-detected face image. For the partial images in the entire to-be-detected face image, the candidate points of the facial key points corresponding to the partial images are respectively determined. Therefore, a calculation amount may be reduced, and the efficiency of determining the candidate points of the facial key points is improved.

Abstract: A face liveness detection method is performed by an electronic device. The method includes: obtaining an initial face image of an object and a face image set of the object captured at N different illumination directions; obtaining N difference images according to the initial face image and the N face images in the face image set; generating a normal map and an albedo map according to the N difference images and the N illumination directions; and determining a face liveness detection result according to the N difference images, the normal map, and the albedo map, the face liveness detection result indicating whether the object has a live face or not. In this application, three-dimensional (3D) geometric information and surface material information of a face image are considered, thereby recognizing the authenticity of the face image, and effectively resisting different face liveness attack manners.

Abstract: A video processing method includes: obtaining a first video frame and a second video frame in a target video, the first video frame being a previous frame of the second video frame; inputting the first video frame and the second video frame to a target neural network, and obtaining a target intermediate video frame output by the target neural network, the target neural network being trained based on an optical flow distillation constraint and a feature consistency constraint; and interpolating the target intermediate video frame between the first video frame and the second video frame.

Abstract: This application provides an image processing method and apparatus. The method includes acquiring an identity feature of a source image and an initial attribute feature of at least one measure of a target image in response to receiving a face change request; inputting the identity feature and the initial attribute feature of the at least one measure into a face change model; iteratively performing feature fusion on the identity feature and the initial attribute feature of the at least one measure by using the face change model to obtain a fusion feature; and generating a target face change image based on the fusion feature by using the face change model, and outputting the target face change image, a face in the target face change image being fused with an identity feature of the source face and a target attribute feature of the target face.

Abstract: Embodiments of this application disclose an image processing method and apparatus, and a computer-readable storage medium. The method includes acquiring an image pair to be processed, and performing image feature extraction on an image to be processed in the image pair to be processed to obtain an image; extracting an association feature from the image feature, the association feature characterizing mutual information between images in the image pair to be processed; identifying a common view image of a common view region in the image to be processed according to the association feature, and calculating a scale difference between common view images; adjusting a size of the common view image based on the scale difference to obtain an adjusted common view image; and extracting at least one common view feature point from each of the adjusted common view images, and processing the image based on the common view feature point.

Abstract: This application provides an image processing method performed by a computer device. The image processing method includes: receiving a face swapping request; acquiring an attribute parameter of the image, an attribute parameter of the target face, and a face feature of the target face, the attribute parameter of the image indicating a three-dimensional attribute of the face in the image; determining a target attribute parameter based on the attribute parameter of the image and the attribute parameter of the target face; determining a target comprehensive feature based on the target attribute parameter and the face feature of the target face; encoding the image to obtain an image encoding feature of the image; migrating the target comprehensive feature to the image encoding feature of the image by normalization to obtain a fusion encoding feature; and decoding the fusion encoding feature to obtain a target face-swapped image including a fusion face.

Abstract: An image super-resolution method includes performing frequency domain transformation on a first image to obtain a spectral feature of the first image, the spectral feature representing a distribution of a grayscale gradient in the first image. The method further includes performing blur kernel prediction based on the spectral feature to obtain a blur kernel of the first image, the blur kernel being a convolution kernel. The method also includes performing super-resolution processing on the first image based on the blur kernel to generate a super-resolved image, a definition of the super-resolved image being higher than a definition of the first image.

Abstract: An de-identification method includes: performing sampling on a target space of a projector in a target network model to obtain N virtual identity vectors, N being a positive integer; extracting, by using an attribute extractor in the target network model, M attribute vectors from a target image, M being a positive integer; and generating, by using a generator in the target network model, a de-identified image of the target image based on the N virtual identity vectors and the M attribute vectors.

Abstract: A facial image processing method includes: obtaining first optical flow information; obtaining, based on the first optical flow information, second optical flow information through an optical flow information prediction sub-model of an image processing model; generating, based on the first facial sample image and the second optical flow information, a predicted image of the first facial sample image through a generator of the image processing model; discriminating the predicted image and the second facial sample image through a discriminator of the image processing model to obtain a first discrimination result and a second discrimination result; and training the image processing model based on the first discrimination result, the second discrimination result, the predicted image, the second facial sample image, the first optical flow information, and the second optical flow information.

Abstract: Disclosed herein are an image detection method and apparatus, a computer-readable storage medium, and a computer device. The method includes iteratively training a plurality of neural network models to obtain a plurality of trained neural network model; and performing detection on an image to be detected using the trained plurality of neural network models to obtain a detection result. Each iteration of training includes: for each of a plurality of sample images, separately inputting the sample image into the neural network models to obtain a fuzzy probability value set, and calculating, based on the fuzzy probability value set and preset label information of the sample image, a loss parameter of the sample image; selecting target sample images based on a distribution of loss parameters of the plurality of sample images; and updating the plurality of neural network models based on the target sample images.

Abstract: Embodiments of this application disclose an image style conversion method performed by an electronic device. The method includes: performing quality enhancement on a first target style image to obtain a second target style image; performing feature extraction on the second target style image to obtain a target style feature; performing migration training on a preset target style conversion model by using a full style conversion model and the target style feature to obtain a target style conversion model; inputting a full style feature, the target style feature, and a to-be-converted image into the target style conversion model, and performing style conversion on the to-be-converted image using the target style conversion model to obtain a target image conforming to a target style.

Abstract: This disclosure relates to image processing method and apparatus. The method includes: processing, with a first generator in an image processing model, a first sample image in a first sample set to obtain a first predicted image; processing, with the first generator, a second sample image in a second sample set to obtain a second predicted image; and training the image processing model according to a difference between the target avatar in the first sample image and the first predicted avatar and a difference between a first type attribute of the sample avatar in the second sample image and a first type attribute of the second predicted avatar.

Abstract: An image processing method and apparatus that includes: performing feature extraction processing on N image regions of a to-be-processed image respectively to obtain feature data corresponding to the N image regions respectively, N being an integer greater than or equal to 1, performing defect detection on the N image regions respectively according to the feature data corresponding to the N image regions respectively to obtain a prediction probability of a defect in each of the N image regions, obtaining attention for each image region, adjusting the prediction probability of the defect in each image region according to the attention for each image region, and generating a prediction result of the to-be-processed image according to the adjusted prediction probability of each image region.