Abstract: An animation file processing method and apparatus, a computer-readable storage medium, and a computer device are provided. The method includes: obtaining a bitmap image sequence corresponding to an original animation file; encoding a differential pixel region between a bitmap image in the bitmap image sequence and a corresponding key bitmap image when the bitmap image is a non-key bitmap image, to obtain an encoded picture corresponding to the bitmap image; and generating an animation export file corresponding to the original animation file according to encoded pictures corresponding to bitmap images in the bitmap image sequence.

Abstract: This application provides a face living body detection method performed by a computing device, the method including: obtaining a first face image of a target detection object in a first illumination condition and a second face image of the target detection object in a second illumination condition, determining a difference image according to the two images, decoupling an object reflectivity and an object normal vector corresponding to the target detection object from a feature map extracted from the difference image, and determining whether the target detection object is a living body according to the object reflectivity and the object normal vector. This method decouples texture information and depth information of a face, and performs living body detection by using decoupled information, which increases the defense capability against 3D attacks, thereby effectively defending against planar attacks and 3D attacks.

Abstract: A computer device obtains a block proposal message and obtains a first block from the block proposal message. In accordance with a determination that a current consensus node of the computer device has a second block in a locked state and a block height of the first block is the same as a block height of the second block, the computer device deletes the block proposal message and obtains block voting information of the second block in a first consensus stage. The locked state is a state of a block that has passed the first consensus stage and has not passed a second consensus stage. The computer device obtains block submission voting information of the second block in the second consensus stage according to the block voting information, and determines a consensus result corresponding to the second block according to the block submission voting information.

Abstract: A video sound-picture matching includes: acquiring a voice sequence; acquiring a voice segment from the voice sequence; acquiring an initial position of a start-stop mark and a moving direction of the start-stop mark from an image sequence; determining an active segment according to the initial position of the start-stop mark, the moving direction of the start-stop mark, and the voice segment; and synthesizing the voice segment and the active segment to obtain a video segment. In a video synthesizing process, the present disclosure uses start-stop marks to locate positions of active segments in an image sequence, so as to match the active segments having actions with voice segments, so that the synthesized video segments are more in line with natural laws of a character during speaking, and have better authenticity.

Abstract: Embodiments of the disclosure provide a vehicle-road collaboration apparatus and method, an electronic device, and a storage medium. The vehicle-road collaboration apparatus includes a service system, which includes a plurality of microservice modules, and each microservice module is configured to process vehicle-road collaboration data to implement a corresponding service function. Thus, scalability and compatibility of a vehicle-road collaboration system are improved.

Abstract: A medical endoscope image recognition method is provided. In the method, endoscope images are received from a medical endoscope. The endoscope images are filtered with a neural network, to obtain target endoscope images. Organ information corresponding to the target endoscope images is recognized via the neural network. An imaging type of the target endoscope images is identified according to the corresponding organ information with a classification network. A lesion region in the target endoscope images is localized according to an organ part indicated by the organ information. A lesion category of the lesion region in an image capture mode of the medical endoscope corresponding to the imaging type is identified.

Abstract: Aspects of the disclosure are directed to a method for use on a blockchain network that includes an accounting node subnetwork having accounting nodes configured to record a data block onto a blockchain and a service node having service nodes configured to verify data blocks recorded by the accounting nodes onto the blockchain. The method can include generating a signature based on transaction information to be included in a data block to be added onto the blockchain by using a key specific to the accounting node. The method can further include adding the transaction information and the generated signature to the data block and adding the data block onto the blockchain, and transmitting the signature to the service nodes in the service node subnetwork, so that the service nodes perform signature verification on the signature based on the key specific to the accounting node.

Abstract: A method includes displaying a first character selection interface of a first account of a multiplayer interactive service, the first character selection interface displaying one or more player accounts and a plurality of candidate virtual characters. The method further includes, in response to receiving in the first character selection interface a character recommendation operation including a selection of a first recommended virtual character and a selection of a second account, transmitting character recommendation information corresponding to the second account. The method further includes displaying, in the first character selection interface of the first account, an indication that the virtual character recommendation was applied to the second account.

Abstract: This application provide a data management method for a blockchain system, a medium, and an electronic device. The system includes an accounting node sub-network and a service node sub-network. The method includes: adding, after an accounting node generates a first data block, first key information used for verifying a block header of a second data block generated after the first data block to a block header of the first data block; generating a signature corresponding to the first data block, and adding the signature corresponding to the first data block to the block header of the first data block; and releasing the block header of the first data block to the service node sub-network, to cause a service node to verify the signature included in the block header of the first data block, and obtaining the first key information after a successful verification to verify the block header of the second data block.

Abstract: This application discloses a video classification model construction method and apparatus, a video classification method and apparatus, a device, and a medium.

Abstract: Embodiments of this application provide an object tracking method performed by a computer device. The method includes, when a target object is lost in a second image frame in a first subsequent image frames, determining, according to a first local feature and in second subsequent image frames starting with the second image frame, a third image frame in which the target object reappears after the target object is lost during the tracking; determining a location of a target object region in the third image frame including the target object; and continuing to track the target object in image frames according to the location of the target object region in the third image frame. Through the object tracking method, a lost object can be detected and repositioned by using an extracted first local feature of the target object, thereby effectively resolving the problem in the existing technical solution.

Abstract: This application provides a pathological section image processing method performed by a computer device. The method includes: obtaining stained images of a pathological section after cell membrane staining; determining cell nucleus positions of cancer cells in a stained image under an ith field of view in the n fields of view; generating a cell membrane description result of the stained image under the ith field of view, the cell membrane description result being used for indicating completeness and staining intensity of the cell membrane staining; determining quantities of cells of types in the stained image under the ith field of view according to the cell nucleus positions and the cell membrane description result; and determining an analysis result of the pathological section according to quantities of the cells of types in the stained images under the n fields of view.

Abstract: Disclosed is a video decoding method, including: obtaining a current video frame, the current video frame being partitioned into a plurality of regions; obtaining a syntax element carried in syntax data corresponding to each of the plurality of regions, the syntax element being used for indicating a resolution used to decode the region, and a plurality of resolutions used to decode the plurality of regions including at least two different resolutions; and decoding the each of the plurality of regions by using the resolution corresponding to the region. The plurality of resolutions are determined according to a transmission bandwidth of a video stream including the current video frame from a source to a destination, e.g., by comparing the transmission bandwidth with a preset bandwidth threshold.

Abstract: A video encoding method and apparatus is provided. The method includes determining a chroma format of a video frame, determining, for a currently decoded first quantization matrix (QM), a size of the first QM according to the determined chroma format and a size of a luma transform block (TB) when the first QM is a chroma QM, and decoding the first QM when the size of the first QM is greater than zero. Thus, a size of a chroma QM is flexibly determined according to a chroma format and a size of a luma TB, rather than a fixed size predefined according to an identifier of the QM, thereby improving flexibility during encoding and decoding of the chroma QM. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also provided.

Abstract: Embodiments of this application disclose a method and for dividing geographical regions performed by a computing device. The method includes: encoding latitudes and longitudes in a first dataset to obtain a second dataset, the first dataset recording N groups of latitudes and longitudes, and the second dataset recording N region numbers, and each of the region numbers being used for representing a corresponding geographical region; obtaining a third dataset from the second dataset, the third dataset recording M groups of the region numbers, corresponding central point latitudes and central point longitudes in the geographical regions represented by the region numbers; and dividing the geographical regions represented by the M groups of region numbers into P regions according to the third dataset, each of the regions including at least one of the geographical regions represented by the corresponding group of region numbers.

Abstract: This application relates to an image recognition model training method, an image recognition method, apparatus, and system. The method includes: obtaining a to-be-recognized image; extracting image feature information of the to-be-recognized image; and obtaining a lesion category recognition result of the to-be-recognized image by using the image feature information of the to-be-recognized image as an input parameter of a preset image recognition model, the image recognition model being trained by using a training image sample set comprising at least one strong-label training image sample, to determine the lesion category recognition result; and the strong-label training image sample representing an image sample having strong-label information, and the strong-label information comprising at least annotation information of a lesion category and a lesion position in the strong-label training image sample.

Abstract: Embodiments of this application disclose a video classification method performed by a computer device and belong to the field of computer vision (CV) technologies. The method includes: obtaining a video; selecting n image frames from the video; extracting respective feature information of the n image frames according to a learned feature fusion policy by using a feature extraction network, the learned feature fusion policy being used for indicating proportions of the feature information of the other image frames that have been fused with feature information of a first image frame in the n image frames; and determining a classification result of the video according to the respective feature information of the n image frames. By replacing complex and repeated 3D convolution operations with simple feature information fusion between adjacent image frames, time for finally obtaining a classification result of the video is therefore reduced, thereby having high efficiency.

Abstract: This application provides a video message generation method and apparatus, an electronic device, and a storage medium. The video message generation method includes: receiving a comment instruction for a video; determining a target frame corresponding to the comment instruction in the video; obtaining input comment content of the target frame; and generating a video message based on the video, the target frame, and the comment content of the target frame.

Abstract: Embodiments of this application disclose method and apparatus for positioning a target audio signal by an audio interaction device, and an audio interaction device The method includes: obtaining audio signals in a plurality of directions in a space, and performing echo cancellation on the audio signal, the audio signal including a target-audio direct signal; obtaining weights of a plurality of time-frequency points in the audio signals, a weight of each time-frequency point indicating, at the time-frequency point, a relative proportion of the target-audio direct signal in the audio signals; weighting time-frequency components of the audio signal at the plurality of time-frequency points separately for each of the plurality of directions by using the weights of the plurality of time-frequency points, to obtain a weighted audio signal energy distribution; and obtaining a sound source azimuth corresponding to the target-audio direct signal in the audio signals accordingly.

Abstract: This application provides a method for training a semantic understanding model, including: obtaining a first training sample set; performing denoising processing on the first training sample set to form a corresponding second training sample set; processing the second training sample set by using a semantic understanding model, to determine initial parameters of the semantic understanding model; processing the second training sample set by using the semantic understanding model in response to the initial parameters of the semantic understanding model, to determine update parameters of the semantic understanding model; and iteratively updating a semantic representation layer network parameter and a task-related output layer network parameter of the semantic understanding model by using the second training sample set and according to the update parameters of the semantic understanding model.