Alerta

IMAGE COMPRESSION AND DECODING, VIDEO COMPRESSION AND DECODING: METHODS AND SYSTEMS

Absstract of: US20260089329A1

A computer-implemented method for lossy image or video compression, transmission and decoding, the method including the steps of (i) receiving an input image at a first computer system; (ii) encoding the input image using a first trained neural network, using the first computer system, to produce a latent representation; (iii) quantizing the latent representation using the first computer system to produce a quantized latent; (iv) entropy encoding the quantized latent into a bitstream, using the first computer system; (v) transmitting the bitstream to a second computer system; (vi) the second computer system entropy decoding the bitstream to produce the quantized latent; (vii) the second computer system using a second trained neural network to produce an output image from the quantized latent, wherein the output image is an approximation of the input image.

MODEL TRAINING METHOD AND APPARATUS, CONSTRUCTION SAFETY EVALUATION METHOD AND APPARATUS, AND DEVICE

Absstract of: WO2026061185A1

The present application discloses a model training method and apparatus, a construction safety evaluation method and apparatus, and a device. The model training method comprises: acquiring sample data; on the basis of association information between training samples and true value labels, determining label missing ratios of the training samples in different dimensions and first weight values of the training samples in different dimensions; determining second weight values of the training samples on the basis of the label missing ratios; and inputting the sample data, the first weight values, and the second weight values into a preset neural network model for training until a loss value of a target loss function of the preset neural network model meets a model convergence condition, so as to obtain a target prediction model. In this way, by improving a weighted loss function, when sample data having a partially missing label is kept, a loss value of a missing label of a sample is calculated with reference to a weight, thereby solving the problem of reduced prediction accuracy caused by missing samples, improving the accuracy of a model, and simultaneously and accurately predicting prediction values of multiple dimensions.

USE OF A CONVOLUTIONAL NEURAL NETWORK TO AUTO-DETERMINE A FLOOR HEIGHT AND FLOOR HEIGHT ELEVATION OF A BUILDING

Absstract of: US20260087344A1

0000 A method using a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.

END-TO-END STREAMING KEYWORD SPOTTING

Absstract of: US20260088023A1

A method for training hotword detection includes receiving a training input audio sequence including a sequence of input frames that define a hotword that initiates a wake-up process on a device. The method also includes feeding the training input audio sequence into an encoder and a decoder of a memorized neural network. Each of the encoder and the decoder of the memorized neural network include sequentially-stacked single value decomposition filter (SVDF) layers. The method further includes generating a logit at each of the encoder and the decoder based on the training input audio sequence. For each of the encoder and the decoder, the method includes smoothing each respective logit generated from the training input audio sequence, determining a max pooling loss from a probability distribution based on each respective logit, and optimizing the encoder and the decoder based on all max pooling losses associated with the training input audio sequence.

DEEP NEURAL NETWORKS (DNN) INFERENCE USING PRACTICAL EARLY EXIT NETWORKS

Absstract of: US20260086912A1

The present disclosure relates to methods and systems for providing inferences using machine learning systems. The methods and systems receive a load forecast for processing requests by a machine learning model and split the machine learning model into a plurality machine learning model portions based on the load forecast. The methods and systems determine a batch size for the requests for the machine learning model portions. The methods and systems use one or more available resources to execute the plurality of machine learning model portions to process the requests and generate inferences for the requests.

OBJECT IDENTIFICATIONS IN IMAGES OR VIDEOS

Absstract of: US20260087646A1

0000 An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of a first object and a second object. The apparatus further includes a memory storage unit to store the raw data. In addition, the apparatus includes a neural network engine to receive the raw data. The neural network engine is to generate a segmentation map and a boundary map. The apparatus also includes a post-processing engine to identify the first object and the second object based on the segmentation map and the boundary map.

CONTROLLING A FUNCTION VIA GAZE DETECTION

Absstract of: US20260086636A1

0000 Aspects of the present disclosure relate to systems and methods for controlling a function of a computing system using gaze detection. In examples, one or more images of a user are received and gaze information may be determined from the received one or more images. Non-gaze information may be received when the gaze information is determined to satisfy a condition. Accordingly, a function may be enabled based on the received non-gaze information. In examples, the gaze information may be determined by extracting a plurality of features from the received one or more images, providing the plurality of features to a neural network, and determining, utilizing the neural network, a location at a display device at which a gaze of the user is directed.

Method And Device For Monitoring Port And Ship In Consideration Of Sea Level

Absstract of: US20260087822A1

The present invention relates to a method for monitoring a harbor performed by a computing device, the method for monitoring the harbor according to an aspect of the present invention comprising: obtaining a harbor image having a first view attribute; generating a segmentation image having the first view attribute and corresponding to the harbor image by performing an image segmentation using an artificial neural network trained to output information, from an input image, related to an object included in the input image; generating a transformed segmentation image having a second view attribute from the segmentation image having the first view attribute based on a first view transformation information used to transform an image having the first view attribute into an image having the second view attribute different from the first view attribute; and calculating berthing guide information of the ship based on the transformed segmentation image.

IMPROVED WELLBORE CONTROL AND MODELS USING IMAGE DATA SYSTEMS AND METHODS

Absstract of: US20260085602A1

Computer implemented methods and systems for testing one or more operational changes in a drill rig includes initiating the one or more operational changes and using, in part, image data of a mechanical mud separation machines (“MMSM”) to detect the impact of the one or more changes. The image data may be processed by a Deep Neural Network to identify objects in the object flow, operational parameters of the MMSM, and wellbore environmental conditions. Additional image data may be selected for additional processing based on the results of the analysis. The results of the test may be used to update the drilling operation or a drilling model.

VIRTUAL IMPACTOR-BASED LABEL-FREE PARTICULATE MATTER DETECTION USING HOLOGRAPHY AND DEEP LEARNING

Absstract of: US20260086013A1

A particulate matter detection device takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides larger particles to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a CMOS image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse and triplicate holograms of the same particle are recorded at a single frame revealing different perspectives of each particle. A deep neural network classifies the particles based on the acquired holographic images. The device was tested using different types of pollen and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ˜700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods.

NEURAL NETWORKS FOR CHANGING CHARACTERISTICS OF VOCALS

Absstract of: EP4715678A2

A messaging system for audio character type swapping. Methods of audio character type swapping include receiving input audio data having a first characteristic and transforming the input audio data to an input image where the input image represents the frequencies and intensities of the audio. The methods further include processing the input image using a convolutional neural network (CNN) to generate an output image and transforming the output image to output audio data, the output audio data having a second characteristic. The input audio and output audio may include vocals. The first characteristics may indicate a male voice and the second characteristics may indicate a female voice. The CNN is trained together with another CNN that changes input audio having the second characteristic to audio having the first characteristic. The CNNs are trained using discriminator CNNs that determine whether audio has a first characteristic or a second characteristic.

INFORMATION GENERATION METHOD AND RELATED APPARATUS

Nº publicación: EP4715669A1 25/03/2026

Applicant:

HUAWEI TECH CO LTD [CN]

Absstract of: EP4715669A1

0001 Embodiments of this application disclose an information generation method and a related apparatus. The method includes: A second device receives a first message and a third message, and sends a second message to a first device. The first message indicates all or a part of a first generator, the third message indicates all or a part of a third generator, an input supported by the first generator includes first information of a first type, an input supported by the third generator includes fourth information of the first type, and the first generator and the third generator are configured to train a neural network corresponding to a second generator; and the second message indicates all or a part of the second generator, and an input supported by the second generator includes the first information and the fourth information. According to embodiments of this application, information collected in a real scenario may be used to train a generation model, to implement communication-assisted detection and detection-assisted communication, so that a communication network develops towards a more intelligent and adaptive direction.