Abstract: A joint optimization method for path selection and gate scheduling in time-sensitive networking comprises S1, a CNC finding a TSN topology, and abstracting same into a network directed graph; S2, a terminal device sending to a CUC a TSN connection request, and the CUC sending same to the CNC; S3, the CNC selecting K shortest paths as alternative paths; S4, the CNC selecting m preferred paths; S5, the CNC finding an optimal transmission path for a TT stream, and finding a proper transmission path for a non-TT stream; S6, CNC completing traversal; S7, configuring a gate control list for the optimal transmission path of each pair of terminal devices; and S8, the CNC encapsulating a computation result into a gate scheduling table, configuring the gate scheduling table to a TSN switch, and then sending a traffic transmission computation result to the TSN terminal device.

Abstract: An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

Abstract: An image synthesis method includes: obtaining a source image including source image identity information for synthesis and a template image including template image background information for synthesis; performing a target synthesis operation on the source image and the template image to obtain an initial synthesized image, the initial synthesized image including the source image identity information, the template image background information, and a partial region to be corrected; performing a target correction operation on the source image, the template image, and the initial synthesized image to obtain a target residual image; and synthesizing the initial synthesized image with the target residual image to generate a target synthesized image.

Abstract: A model training method includes obtaining a training sample set including a triplet training sample that includes a source image, a template image, and a true value image, performing face swapping on the source image and the template image through a first image processing model having a re-parameterization structure to obtain a face-swapped image, obtaining a second image processing model corresponding to the first image processing model and being a pre-trained image processing model, calculating a fusion loss function of the first image processing model according to the second image processing model, the first face-swapped image, and the true value image, training the first image processing model according to the fusion loss function, and determining a model parameter of the first image processing model in response to a training convergence condition of the first image processing model being reached.

Abstract: An image processing method, performed by a computer device, includes: obtaining an input image to be processed; performing face detection on the input image to obtain an input face image to be processed including at least one defect element relating to a skin element; and inputting the input face image into an image processing model to obtain a target face image corresponding to the input face image without a first defect element amongst the at least one defect element, wherein a training sample of the image processing model includes a first face image with a first face distortion degree less than a preset threshold and that is annotated with the first defect element.

Abstract: A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

Abstract: A face detection method includes: acquiring a target image; invoking a face detection network, and processing the target image by using a feature extraction structure of the face detection network, to obtain original feature maps corresponding to the target image; the original feature maps having different resolutions; processing the original feature maps by using a feature enhancement structure of the face detection network, to obtain an enhanced feature map corresponding to each original feature map; the feature enhancement structure being obtained by searching a search space, and the search space used for searching the feature enhancement structure being determined based on a detection objective of the face detection network and a processing object of the feature enhancement structure; and processing the enhanced feature map by using a detection structure of the face detection network, to obtain a face detection result of the target image.

Abstract: The present invention provides an asphaltene dispersion stabilizer and a preparation method therefor, and relates to the technical field of oilfield additives. The asphaltene dispersion stabilizer is prepared by the following method: taking iminodisuccinic acid or salt thereof, and performing Michael addition reaction on the iminodisuccinic acid or the salt thereof and unsaturated sulfonate to prepare a first intermediate; taking the first intermediate, and performing amidation reaction on the first intermediate and a first monomer under the action of a catalyst to obtain a second intermediate; and preparing a dendrimer by taking the second intermediate as a core and taking a second monomer and a third monomer as raw materials, wherein the dendrimer is the asphaltene dispersion stabilizer, and the generation number of the dendrimer is 0.5 generations, 1.5 generations or 2.5 generations. The asphaltene dispersion stabilizer of the present invention has a good dispersion stabilization effect on asphaltene.

Abstract: An image generation method, performed by an electronic device includes obtaining an original face image frame, audio driving information, and emotion driving information, performing spatial feature extraction on the original face image frame to obtain an original face spatial feature corresponding to the original face image frame; performing feature interaction processing on the audio driving information and the emotion driving information to obtain a face local pose feature of the to-be-adjusted object issuing the voice content with the target emotion; and performing, based on the original face spatial feature and the face local pose feature, face reconstruction processing on the to-be-adjusted object to generate a target face image frame.

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: 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: 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.