Abstract: Embodiments of this application provide a peer-to-peer network scheduling method. The method includes: obtaining a fragment file list, where the fragment file list includes information about a plurality of fragment files, each fragment file includes one or more segments, and each segment corresponds to one segment number; downloading a missing segment in a target fragment file from another client device based on a downloading progress of downloading each segment of the target fragment file by each client device; and when a player requests the target fragment file, stopping downloading the missing segment in the target fragment file from the another client device, and downloading the missing segment in the target fragment file from a CDN server. According to the embodiments of this application, a computation amount and resource consumption of a Tracker server can be reduced, and data transmission efficiency of a P2P network can be improved.

Abstract: Embodiments of this application provides a peer-to-peer network scheduling method. The method includes: obtaining a fragment file list, where the fragment file list includes information about a plurality of fragment files, each fragment file includes one or more segments, and each segment corresponds to one segment number; downloading one or more target segments corresponding to one or more target segment numbers from a CDN server based on the fragment file list and the one or more target segment numbers, where the one or more target segment numbers are located in a target segment list; adjusting the target segment list based on information about data obtained from and/or sent to another client device, to obtain an adjusted target segment list; and downloading one or more target segments corresponding to one or more target segment numbers in the adjusted target segment list from the CDN server.

Abstract: Disclosed in embodiments of the present disclosure are a method and apparatus for virtual fitting. A specific implementation of the method comprises: receiving a fitting request comprising a model picture and a user image; performing human body positioning and surface coordinate analysis on the model picture and the user image respectively; performing clothing segmentation on the model picture and the user image respectively; on the basis of the clothing segmentation result and the surface coordinate analysis result, covering the pixels corresponding to a piece of clothing in the model picture to corresponding positions in the user image to obtain a synthesized image and information to be completed; and inputting the synthesized image, the positioning result of the user image, and said information into a pre-trained image completion network to obtain a completed image.

Abstract: A method for determining the pore size and pore size distribution of a filtration membrane comprises: selecting a group of fluorescent pellets having different diameters and emission wavelengths as a reference; plotting the standard curve between the concentration and fluorescence intensity for each fluorescent pellet at its emission wavelength; uniformly dispensing a group of fluorescent pellets as a reference substance in water to prepare a mixed suspension of which the mass concentration of each fluorescent pellet; using the filter membrane to be tested to perform one-time filtration on the mixed suspension prepared, then performing fluorescence detection on the obtained filtrate, calculating the concentration of each fluorescent pellet in the filtrate the retention rate of the filtration membrane to be tested for each fluorescent pellet; and calculating the pore size and pore size distribution of the filter membrane to be tested accordingly.

Abstract: Provided are a single-domain antibody against human programmed death-ligand 1 (PD-L1) and application thereof. The PD-L1 binding molecule of the present invention can be used for treating and/or preventing, or diagnosing PD-L1 related diseases such as tumors.

Abstract: The present disclosure describes techniques for generating live streaming video data and playing live streaming video. The techniques comprise obtaining live broadcasting video data; identifying a target object in a video frame; determining a target area based on the target object in the video frame; encoding data indicative of the target area based on a predetermined format for supplementary enhancement information to obtain encoded target area data; and adding the encoded target area data to a data packet corresponding to the video frame, wherein the data packet is encoded based on a predetermined video data format, and wherein the predetermined video data format and the predetermined format for supplementary enhancement information are mutually compatible data encoding formats.

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: 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 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: 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: Disclosed are Aspergillus oryzae aged cheese and a preparation method thereof, wherein the preparation method of the Aspergillus oryzae cheese comprises the following steps: (1) inoculating raw milk after sterilization with lactic acid bacteria fermentation agent and Aspergillus oryzae spore powder, fermenting to a pH value of 6.3-6.5, wherein an additive amount of the Aspergillus oryzae spore powder is 0.001‰-0.008‰‰ of a mass of the raw milk; (2) adding rennin and obtaining curd after curding; (3) cutting the curd to obtain curd blocks; and (4) moulding the curd blocks after expelling whey, and performing pickling and maturing after demoulding, wherein 1-7 days are taken for maturing under 20-30° C.; 7-15 days are taken for maturing under 15-20° C.; 10-20 days are taken for continuing maturing under 8-12° C.; and relative humidity of an environment for maturing is 85%-95%.

Abstract: A computing device acquires an original image sequence. The device performs image preprocessing on the original image sequence to obtain a feature map sequence and a confidence map sequence that are corresponding to the original image sequence. The device performs feature fusion on the feature map sequence based on the confidence map sequence, to obtain a target fused feature map corresponding to a target original image frame in the original image sequence. The device reconstructs the target original image frame based on the target fused feature map to obtain a target reconstructed image frame. Credibility supervision at the pixel level is performed on features in the feature fusion process to guide the fusion of image features with high credibility, thereby improving the image quality of a reconstructed image.

Abstract: This application discloses a face image processing method performed by an electronic device. The method includes: acquiring a face image of a source face and a face template image of a template face; performing three-dimensional face modeling on the face image and the face template image to obtain a three-dimensional face image feature of the face image and a three-dimensional face template image feature of the face template image; fusing the three-dimensional face image feature and the three-dimensional face template image feature to obtain a three-dimensional fusion feature; performing face replacement feature extraction on the face image based on the face template image to obtain an initial face replacement feature; transforming the initial face replacement feature based on the three-dimensional fusion feature to obtain a target face replacement feature; and replacing the template face with the source face based on the target face replacement feature to obtain a target face image.

Abstract: An image processing method includes performing additional image feature extraction on a training source face image to obtain a source additional image feature, performing identity feature extraction on the training source face image to obtain a source identity feature, inputting a training template face image into an encoder in a to-be-trained face swapping model to obtain a face attribute feature, inputting the source additional image feature, the source identity feature, and the face attribute feature into a decoder in the face swapping model for decoding to obtain a decoded face image, obtaining a target model loss value based on an additional image difference between the decoded face image and a comparative face image, and adjusting the model parameters of the encoder and the decoder based on the target model loss value to obtain the trained face swapping model.

Abstract: The present disclosure describes techniques of synchronizing subtitles in live broadcast The disclosed techniques comprise obtaining a source signal and a simultaneous interpretation signal in a live broadcast; performing voice recognition on the simultaneous interpretation signal in real-time to obtain corresponding translation text; delaying the simultaneous interpretation signal to obtain a first delayed signal; delaying the source signal to obtain a second delayed signal; obtaining proofreading results of the first delayed signal and the corresponding translation text; determining proofread subtitles based on the proofreading results; and sending the proofread subtitles and the second delay signal to a live display interface.

Abstract: Disclosed in embodiments of the present disclosure are a method and apparatus for virtual fitting. A specific implementation of the method comprises: receiving a fitting request comprising a model picture and a user image; performing human body positioning and surface coordinate analysis on the model picture and the user image respectively; performing clothing segmentation on the model picture and the user image respectively; on the basis of the clothing segmentation result and the surface coordinate analysis result, covering the pixels corresponding to a piece of clothing in the model picture to corresponding positions in the user image to obtain a synthesized image and information to be completed; and inputting the synthesized image, the positioning result of the user image, and said information into a pre-trained image completion network to obtain a completed image.

Abstract: The present disclosure describes techniques for generating live streaming video data and playing live streaming video. The techniques comprise obtaining live broadcasting video data; identifying a target object in a video frame; determining a target area based on the target object in the video frame; encoding data indicative of the target area based on a predetermined format for supplementary enhancement information to obtain encoded target area data; and adding the encoded target area data to a data packet corresponding to the video frame, wherein the data packet is encoded based on a predetermined video data format, and wherein the predetermined video data format and the predetermined format for supplementary enhancement information are mutually compatible data encoding formats.

Abstract: The processing method comprises: acquiring a first image set corresponding to a product category; labelling at least one region of interest for each image in the first image set, wherein each region of interest is used for representing an object; acquiring a scanning window; using the scanning window to scan the regions of interest, acquiring scanning results, and placing the scanning results into a sample training set; establishing an illustration frame acquisition model by taking the first image set as input and taking the sample training set as output; acquiring an image to be processed, and using the illustration frame acquisition model to acquire a target webpage illustration frame corresponding to the image to be processed; and cutting, according to the target webpage illustration frame, the image to be processed so as to obtain a target webpage illustration.

Abstract: The present disclosure describes techniques of synchronizing subtitles in live broadcast The disclosed techniques comprise obtaining a source signal and a simultaneous interpretation signal in a live broadcast; performing voice recognition on the simultaneous interpretation signal in real-time to obtain corresponding translation text; delaying the simultaneous interpretation signal to obtain a first delayed signal; delaying the source signal to obtain a second delayed signal; obtaining proofreading results of the first delayed signal and the corresponding translation text; determining proofread subtitles based on the proofreading results; and sending the proofread subtitles and the second delay signal to a live display interface.