IMAGE RECOGNITION METHOD AND APPARATUS, AND STORAGE MEDIUM

    公开(公告)号:US20230102422A1

    公开(公告)日:2023-03-30

    申请号:US17807375

    申请日:2022-06-16

    Abstract: Provided is an image recognition method. The method includes determining subject decoded features of a to-be-detected image and an original interaction decoded feature of a subject interactive relationship in the to-be-detected image; determining subject decoded features associated with the original interaction decoded feature, and updating the original interaction decoded feature by using the associated subject decoded features so as to obtain a new interaction decoded feature; and according to the subject decoded features of the to-be-detected image and the new interaction decoded feature, determining at least two subjects to which the subject interactive relationship in the to-be-detected belongs.

    DETECTION OF ROAD CHANGE
    142.
    发明申请

    公开(公告)号:US20230101388A1

    公开(公告)日:2023-03-30

    申请号:US18076230

    申请日:2022-12-06

    Abstract: A method for detecting a road change is provided. The method includes: obtaining a road image comprising a road to be detected; extracting a road region in the road image as a target region, wherein the road region corresponds to where the road to be detected is located; obtaining a target geographical location of the target region; determining a reference region for the target region from pre-stored road regions based on the target geographical location; calculating a similarity between the target region and the reference region; and determining, based on the similarity, whether a passability of the road to be detected is changed. By applying the solution provided by the embodiments of the present disclosure, efficiency of road change detection can be improved.

    Resource Tapping Method, Resource Tapping Apparatus and Electronic Device

    公开(公告)号:US20230092978A1

    公开(公告)日:2023-03-23

    申请号:US17992550

    申请日:2022-11-22

    Abstract: This disclosure provides a resource tapping method, a resource tapping apparatus and an electronic device, and relates to the field of computer technology, in particular to the technical field of artificial intelligence, such as deep learning and machine learning. A specific implementation is as follows: obtaining operation data in M resource dimensions of a target cabinet, the M resource dimensions including a power resource, where M is a positive integer; determining a target power over-allocation value of the target cabinet based on the operation data, the target power over-allocation value being used for indicating an allowable power increment on the basis of a power rating of the target cabinet; and determining, based on the target power over-allocation value, a first quantity of additional servers deployable in the target cabinet.

    SEMANTIC UNDERSTANDING METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM

    公开(公告)号:US20230089268A1

    公开(公告)日:2023-03-23

    申请号:US18060692

    申请日:2022-12-01

    Abstract: A semantic understanding method, includes: acquiring a query statement and a preceding dialogue; rewriting the query statement based on a preset rule to generate a target query statement if it is recognized that the query statement meets a rule rewriting condition according to the query statement and the preceding dialogue; rewriting the query statement based on a rewriting model to generate the target query statement if it is recognized that the query statement does not meet the rule rewriting condition according to the query statement and the preceding dialogue; and performing intention recognition according to the target query statement to generate an intention recognition result.

    METHOD OF GENERATING TEXT, METHOD OF TRAINING MODEL, ELECTRONIC DEVICE, AND MEDIUM

    公开(公告)号:US20230084438A1

    公开(公告)日:2023-03-16

    申请号:US17992436

    申请日:2022-11-22

    Abstract: A method of generating a text, a method of training a text generation model, an electronic device, and a storage medium, which relate to a field of a computer technology, in particular to fields of deep learning and natural language processing technologies. A specific implementation solution includes: determining a reference feature representation of a target semantic information; determining, based on the reference feature representation and at least one predetermined logical character, at least one sentence latent representation respectively corresponding to the at least one predetermined logical character; and generating a target text content based on the at least one sentence latent representation.

    MOTION SEARCH METHOD AND APPARATUS, ELECTRONIC DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20230081957A1

    公开(公告)日:2023-03-16

    申请号:US17943025

    申请日:2022-09-12

    Abstract: A method includes: performing a first diamond search according to an initial point determined from points in a search window, a search step size being incremented by ith power of 2, i being a natural number, 0≤i≤N; and performing the following first processing: acquiring an updated initial point and an optimization range, the optimization range being less than 2N; performing a second diamond search according to the initial point, wherein, prior to a search with a search step size larger than the optimization range, if it is determined that an ending condition is met, the diamond search is ended and a corresponding second optimal point is determined; and determining a required optimal motion vector according to the second optimal point if the second optimal point meets a predetermined requirement, and otherwise, repeating the first processing.

    METHOD AND APPARATUS FOR TRAINING SEMANTIC SEGMENTATION MODEL, AND METHOD AND APPARATUS FOR PERFORMING SEMANTIC SEGMENTATION ON VIDEO

    公开(公告)号:US20230079275A1

    公开(公告)日:2023-03-16

    申请号:US17985000

    申请日:2022-11-10

    Abstract: The present disclosure provides a method and apparatus for training a semantic segmentation model and a method and apparatus for performing a semantic segmentation on a video. The method comprises: acquiring a training sample set, wherein a training sample in the training sample set comprises at least one sample video stream and a pixel-level annotation result of the sample video stream; modeling a spatiotemporal context between video frames in the sample video stream using an initial semantic segmentation model to obtain a context representation of the sample video stream; calculating a temporal contrastive loss based on the context representation of the sample video stream and the pixel-level annotation result of the sample video stream; and updating a parameter of the initial semantic segmentation model based on the temporal contrastive loss to obtain a trained semantic segmentation model.

    TRAINING METHOD AND APPARATUS FOR A DISTRIBUTED MACHINE LEARNING MODEL, DEVICE AND MEDIUM

    公开(公告)号:US20230078726A1

    公开(公告)日:2023-03-16

    申请号:US17932405

    申请日:2022-09-15

    Inventor: Bo JING

    Abstract: Provided are a training method and apparatus for a distributed machine learning model, a device and a medium. The training method includes: acquiring a first homomorphic encryption intermediate parameter and a second homomorphic encryption intermediate parameter; generating a first interference parameter, and forming a first encryption interference parameter by encrypting the first interference parameter by using a second homomorphic public key of a second participant; performing calculation based on the first homomorphic encryption intermediate parameter, the second homomorphic encryption intermediate parameter, the first encryption interference parameter and the homomorphic calculation function of a first submodel to generate a first encryption key parameter.

    SOUND SOURCE LOCALIZATION MODEL TRAINING AND SOUND SOURCE LOCALIZATION METHOD, AND APPARATUS

    公开(公告)号:US20230077816A1

    公开(公告)日:2023-03-16

    申请号:US17658513

    申请日:2022-04-08

    Abstract: The present disclosure provides a method for training sound source localization model and a sound source localization method, and relates to the field of artificial intelligence technologies such as voice processing and deep learning. The method for training sound source localization model method includes: obtaining a sample audio according to an audio signal including a wake-up word; extracting an audio feature of at least one audio frame in the sample audio, and marking a direction label and a mask label of the at least one audio frame; and training a neural network model by using the audio feature of the at least one audio frame and the direction label and the mask label of the at least one audio frame, to obtain a sound source localization model. The sound source localization method includes: acquiring a to-be-processed audio signal, and extracting an audio feature of each audio frame in the to-be-processed audio signal; inputting the audio feature of each audio frame into a sound source localization model, to obtain sound source direction information outputted by the sound source localization model for each audio frame; determining a wake-up word endpoint frame in the to-be-processed audio signal; and obtaining a sound source direction of the to-be-processed audio signal according to sound source direction information corresponding to the wake-up word endpoint frame.

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