Abstract:
This application provides a training method and apparatus for an image processing model, an electronic device, and a storage medium. The method includes: obtaining a plurality of multimodal images used as training samples, types of the multimodal images including full-modality images and missing-modality images; invoking, based on each of the multimodal images, an initialized image processing model to execute a first training task for reconstructing the full-modality image, the image processing model outputting a first full-modality reconstructed image in a process of executing the first training task; performing image completion processing on each of first full-modality reconstructed images based on the full-modality image, to obtain a full-modality template image; determining a consistency loss between a multimodal image pair and the full-modality template image; and invoking, based on each of the multimodal images, a trained image processing model to execute a second training task for segmenting each of the multimodal images, and using the consistency loss as a constraint condition in the second training task.
Abstract:
A region division includes: determining a plurality of merchants in a target region, and constructing a merchant relationship network of the target region according to merchant information of the plurality of merchants, the merchant information including geographic information of the merchants, and the merchant relationship network being used for identifying an association relationship among the plurality of merchants; determining business districts corresponding to the plurality of merchants based on the merchant relationship network; and determining a business district boundary of each business district according to the geographic information of the merchants included in each business district.
Abstract:
A computer device performs feature extraction on two-dimensional medical images included in a three-dimensional medical image, to obtain image features corresponding to the two-dimensional medical images. The three-dimensional medical image are obtained by continuously scanning a target tissue structure. The computer device determines offsets of the two-dimensional medical images in a target direction based on the image features. The computer device performs feature alignment on the image features based on the offsets, to obtain aligned image features. The computer device performs three-dimensional segmentation on the three-dimensional medical image based on the aligned image features, to obtain three-dimensional layer distribution of the target tissue structure in the three-dimensional medical image.
Abstract:
A voice processing method and device, the method comprising: detecting a current voice application scenario in a network (S1); determining the voice quality requirement and the network requirement of the current voice application scenario (S2); based on the voice quality requirement and the network requirement, configuring voice processing parameters corresponding to the voice application scenario (S3); and according to the voice processing parameters, conducting voice processing on the voice signals collected in the voice application scenario (S4).
Abstract:
Audio encoding methods/terminals, audio decoding methods/terminals, and audio codec systems are pro vided, A plurality of audio signals that are continuous is obtained, it is determined whether each audio signal of the plurality of audio signals includes a designated signal type, according to an audio parameter of each audio signal. A marked audio encoding stream is obtained by performing a marking to each audio signal as having or not having the designated signal type. The marking is used, at a decoding terminal, to perform an enhancement-process to one or more audio signals having the designated signal type. The enhancement-process is not performed to audio signals that do not have the designated signal type.
Abstract:
Provided are a sound effect processing method and device, a plug-in unit manager and a sound effect plug-in unit, which belong to the field of information technology processing. The sound effect processing method comprises: invoking a pre-loaded plug-in unit manager to acquire a sound effect processing parameter supported by each preloaded sound effect plug-in unit (101); acquiring a sound effect configuration file pre-configured by the plug-in unit manager (102); displaying sound effect index identifiers corresponding to various sound effect modes (103); according to a selected sound effect index identifier, determining a selected sound effect mode, and according to an adjustment interface, acquiring adjusted parameter control data (104); sending the adjusted parameter control data to a selected sound effect plug-in unit (105); and invoking the plug-in unit manager to send data to be processed to the selected sound effect plug-in unit, and according to the adjusted parameter control data, conducting sound effect processing on the data to be processed by the selected sound effect plug-in unit (106). By means of the present invention, data to be processed is processed by a sound effect plug-in unit according to adjusted parameter control data, so that sound effect processing is realized without installing hardware. Moreover, for different operating systems, it is not necessary to re-add codes, so that costs of the sound effect processing are reduced, and an application range of the sound effect processing is expanded. In addition, the sound effect can be modified by only modifying a sound effect configuration file, thereby increasing the efficiency of sound effect processing.
Abstract:
A voice processing method and device, the method comprising: detecting a current voice application scenario in a network (S1); determining the voice quality requirement and the network requirement of the current voice application scenario (S2); based on the voice quality requirement and the network requirement, configuring voice processing parameters corresponding to the voice application scenario (S3); and according to the voice processing parameters, conducting voice processing on the voice signals collected in the voice application scenario (S4).