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METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING

Resumen de: US20260289311A1

A method and apparatus with object estimation model training is provided. The method include generating a cross-correlation loss based on a first feature vector, generated using an interim first neural network (NN) model provided an input based on first input data about a target object, and a second feature vector generated using a trained second neural network provided another input based on second input data about the target object; and generating a trained first NN model, including training the interim first NN model based on the cross-correlation loss.

FEATURE MAP COMPRESSION METHOD AND APPARATUS

Resumen de: US20260292179A1

0000 A neural network-based image processing method and apparatus according to an embodiment of the present invention may: acquire a feature tensor from an input image by using a first neural network including a plurality of neural network layers; acquire a symbol tensor by performing quantization on the acquired feature tensor; and generate a bitstream by performing entropy encoding on the basis of the symbol tensor.

FIXED-POINT MULTIPLICATION FOR NETWORK QUANTIZATION

Resumen de: US20260289307A1

0000 Techniques for training a neural network having a plurality of computational layers with associated weights and activations for computational layers in fixed-point formats include determining an optimal fractional length for weights and activations for the computational layers; training a learned clipping-level with fixed-point quantization using a PACT process for the computational layers; and quantizing on effective weights that fuses a weight of a convolution layer with a weight and running variance from a batch normalization layer. A fractional length for weights of the computational layers is determined from current values of weights using the determined optimal fractional length for the weights of the computational layers. A fixed-point activation between adjacent computational layers is related using PACT quantization of the clipping-level and an activation fractional length from a node in a following computational layer. The resulting fixed-point weights and activation values are stored as a compressed representation of the neural network.

METHOD AND SYSTEM FOR SINGLE PASS OPTICAL CHARACTER RECOGNITION

Resumen de: US20260289995A1

A computer implemented method of performing single pass optical character recognition (OCR) including a fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The method includes preprocessing an input image having machine printed characters; extracting two or more image features using a plurality of convolutional layers in the FCN engine; transforming feature maps from the plurality of convolutional layers to a size of the input image using a transposed convolution layer of the FCN engine; generating a multi-channel pixel-level probability output based on the transformed feature maps; scaling the multi-channel pixel-level probability output; generating word boxes for the machine printed characters; determining each character of the machine printed characters; and transmitting, for display, the machine printed characters.

METHOD FOR TRAINING A NEURAL NETWORK TO CONTROL A TECHNICAL SYSTEM

Resumen de: US20260289299A1

0000 A method for training a neural network to control a technical system. The method includes ascertaining a partitioning of a control function operating on an input space into a set of first affine functions, each operating on a particular first subset of the input space; training the neural network to approximate the control function in a plurality of iterations, comprising, for each iteration: ascertaining, for each first subset, how the neural network partitions the first subset into second subsets such that it behaves like a particular second affine function on each second subset; ascertaining, for each of the second subsets, an approximation error between the neural network and the control function; ascertaining an overall approximation error between the neural network and the control function from the ascertained approximation errors; and adapting the neural network to reduce the overall approximation error.

Systems and Methods to Learn Two-Level System Defects in Quantum Systems

Resumen de: US20260289366A1

0000 Example aspects of the present disclosure provide systems and methods to learn machine-learned model parameters for models of quantum computing systems. In particular, example aspects of the present disclosure are directed to systems and methods to learn a deep neural network configured to predict parameter values for a physical model that models quantum dynamics of interactions between one or more qubits of a quantum gate and one or more two-level-system (TLS) defects during operation of the quantum gate through use of an evolutionary algorithm.

LOW POWER ANALOG CIRCUITRY FOR ARTIFICIAL NEURAL NETWORKS

Resumen de: US20260290329A1

0000 A low power analog Long Short-Term Memory (LSTM) recurrent neural network has an input layer, an array of Adaptive Filter Unit for Analog LSTM, a linear projection layer, and an output layer. The output layer has multiple nonlinear amplifiers, a nonlinear element with a sigmoidal input-output characteristic function, and a time-constant adjustable, nonlinear, low pass filter that provides the memory function of the LSTM. The LSTM memory is used with mismatch-robust weights determined by learning by computation of optimal weights values, wherein the objective function minimizes misdetection probability, and used to process a signal to detect events.

INFERENCE OF CARTILAGE SEGMENTATION IN COMPUTED TOMOGRAPHY SCANS

Resumen de: US20260289948A1

Some examples are directed to a processor-implemented methods of training a neural network for soft-tissue labeling of computed tomography (CT) images, neural networks trained according to one or more of the processor-implemented methods, processor-implemented methods of soft-tissue-labeling a CT image, and systems for labeling soft-tissue in computed tomography images.

METHOD AND APPARATUS FOR ENHANCING SPEECH USING ARTIFICIAL INTELLIGENCE

Resumen de: US20260290366A1

0000 In a speech enhancement method performed by an electronic device using artificial intelligence according to an embodiment of the present disclosure, the method may include: determining a hidden representation for a sequence of an input speech signal in a first neural network; determining speech recognition information based on the hidden representation; receiving the speech recognition information and an input data block in a second neural network; and generating an output data block by combining the speech recognition information and the input data block. (FIG. 8)

DISTRIBUTED NEURAL NETWORK TRAINING SYSTEM

Resumen de: US20260288904A1

Apparatuses, systems, and techniques to train a machine-learned model. In at least one embodiment, a plurality of training clients each obtain an exclusive right to update a model in turn, and each client trains said model with training data not accessible to other training clients.

SYSTEM AND METHOD FOR IMPROVING CARDIOVASCULAR HEALTH OF HUMANS

Resumen de: US20260290539A1

An estimate of a functional capacity such as VO2Max is made by applying the vital signs of a monitored human to a trained encoding neural network producing a cardio profile vector. The vector is applied to a trained functional capacity (VO2Max) neural network to estimate the functional capacity. Once estimated, an action is taken.

VIDEO SYNTHESIS WITHIN A MESSAGING SYSTEM

Resumen de: US20260289238A1

0000 Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for video synthesis. The program and method provide for accessing a primary generative adversarial network (GAN) comprising a pre-trained image generator, a motion generator comprising a plurality of neural networks, and a video discriminator; generating an updated GAN based on the primary GAN, by performing operations comprising identifying input data of the updated GAN, the input data comprising an initial latent code and a motion domain dataset, training the motion generator based on the input data, and adjusting weights of the plurality of neural networks of the primary GAN based on an output of the video discriminator; and generating a synthesized video based on the primary GAN and the input data.

USING NEURAL NETWORKS TO GENERATE IMAGES

Resumen de: US20260289736A1

0000 Apparatuses, systems, and techniques to blend two or more images based on confidence values of objects within said two or more images. In at least one embodiment, one or more confidence values in one or more images are generated using one or more neural networks that are used, for example, to blend two or more images to be displayed.

SHALE FRACTURE SEISMIC IDENTIFICATION METHOD BASED ON 3D U-NET CONVOLUTIONAL NEURAL NETWORK COMBINED WITH ANT TRACKING

Resumen de: US20260289053A1

The invention relates to a shale fracture seismic identification method based on a 3D U-Net convolutional neural network combined with ant tracking. The method establishes a geological model of shale fractures using single-well data and performs seismic forward modeling to determine the advantageous frequency band for fracture identification. Spectral-peak decomposition is applied to obtain the advantageous frequency-band data volume, which is processed using a 3D U-Net convolutional neural network and ant-tracking computation to generate a 3D U-Net Ant Tracking volume. The results are verified using microseismic data, and along-layer attributes of the 3D U-Net Ant Tracking volume are extracted to determine the regional planar distribution characteristics of shale fractures. The method effectively reduces exploration costs by combining single-well and seismic data, significantly improves seismic resolution through integrated application of seismic forward modeling, spectral-peak decomposition, and advantageous frequency-band data computation.

METHOD FOR SECURING A COMPUTER SYSTEM AGAINST INTEGRITY ATTACKS

Resumen de: EP4811192A1

0001 The present invention relates to a method for securing against adversarial and backdoor attacks a computer system including a memory storing a plurality of benign images (501) and using at least one neural network comprising an input layer, hidden layers, and an output layer generating an embedding or a classification result into a set of output classes for an input image presented at the input layer, said method being performed by said computer system and comprising, before or during the inference phase: - splitting at least one benign image (501) among said stored benign images into a plurality of zones according to at least one predefined method; and at the inference phase: - acquiring an image (502); - splitting the acquired image into a plurality of zones according to said at least one predefined method; -generating at least one composite image (503, 504, 505, 506) by replacing, in at least one split benign image among said stored benign images a zone of the split benign image corresponding to a zone of the split acquired image with said zone of the split acquired image; -applying said at least one neural network to said at least one generated composite image for generating a result from said output layer of said at least one neural network; -comparing said generated result and a result generated by applying said at least one neural network to said acquired image; - based on said comparison, detecting an adversarial or backdoor attack and performing a predetermi

GENERATING TRAINING DATA FOR ESTIMATING MATERIAL PROPERTY PARAMETER OF FABRIC AND ESTIMATING MATERIAL PROPERTY PARAMETER OF FABRIC

Resumen de: EP4811314A2

Estimating a material property parameter of fabric involves receiving information including a three-dimensional (3D) contour shape of fabric placed over a 3D geometric object, estimating a material property parameter of the fabric used for representing drape shapes of 3D clothes made by the fabric by applying the information to a trained artificial neural network, and providing the material property parameter of the fabric.

OPEN-VOCABULARY OBJECT DETECTION IN IMAGES

Resumen de: EP4811306A2

0001 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object detection. In one aspect, a method comprises: obtaining: (i) an image, and (ii) a set of one or more query embeddings, wherein each query embedding represents a respective category of object; processing the image and the set of query embeddings using an object detection neural network to generate object detection data for the image, comprising: processing the image using an image encoding subnetwork of the object detection neural network to generate a set of object embeddings; processing each object embedding using a localization subnetwork to generate localization data defining a corresponding region of the image; and processing: (i) the set of object embeddings, and (ii) the set of query embeddings, using a classification subnetwork to generate, for each object embedding, a respective classification score distribution over the set of query embeddings.

COMPUTER-IMPLEMENTED METHOD FOR APPROXIMATING AT LEAST TWO UNKNOWN VARIABLES OF A SET OF PARTIAL DIFFERENTIAL EQUATIONS, HYBRID COMPUTING SYSTEM, COMPUTER PROGRAM PRODUCT AND COMPUTER-READABLE MEDIUM

Resumen de: CA3301830A1

The invention relates to a computer-implemented method for approximating at least two unknown variables (10) of a set of partial differential equations using a quantum physics-informed neural network (100), the method comprising the steps: providing a numerical grid (20) having grid coordinates (30) at which the unknown variables (10) are to be approximated; providing a quantum physics-informed neural network (100), the quantum physics-informed neural network (100) comprising a hybrid network (110) for each unknown variable (10), wherein the hybrid networks (110) are not interconnected to each other, each hybrid network (110) having a quantum network (120) having at least one quantum layer(130) and a classical network (140) having at least one classical layer (150), wherein the respective quantum network (120) and the respective classical network (140) are not interconnected; inputting the grid coordinates (30) into the quantum physics-informed neural network (100), such that the grid coordinates (30) are input to each hybrid network (110); and computing the output (Hout) of each hybrid network (110) for each grid coordinate (30), each output (Hout) corresponding to a different unknown variable (10) to be approximated at the grid coordinate (30), wherein the output (Hout) of each hybrid network (110) is obtained by combining an output of the respective quantum network (Qout) and an output of the respective classical network (Cout) of said hybrid network (110). The invention f

SYSTEM AND METHOD FOR SEGMENTATION OF MAGNETIC RESONANCE IMAGES USING DEEP LEARNING WITH DOMAIN ADAPTATION

Resumen de: WO2026193213A1

Systems and methods disclosed herein are directed to segmentation of magnetic resonance (MR) images of a subject and include receiving at least one MR image of the subject, providing the at least one MR image of the subject to a segmentation system comprising a neural network-based unsupervised domain adaptation (UDA) model, a foundation model, and a mask- guided semi-supervised (MGSS) network, generating a segmentation of a region of interest of the at least one MR image of the subject using the segmentation system and displaying the segmentation of the at least one MR image of the subject.

3D MULTI-SENSOR PERCEPTION AND OBJECT TRACKING

Resumen de: US20260279058A1

In various examples, the embodiments disclosed herein describe a 3D perception-based machine learning framework for generating behavior data for detected objects (e.g., objects, persons, animals, machines, etc.) using multi-view optical image data. The framework processes multi-view optical image data from multiple camera sensors, and neural network-based spatial-temporal processing, to generate object behavior data that may be used to facilitate accurate real-time multi-target multi-camera (MTMC) object tracking across a monitored environment. The framework may comprise one or more machine learning models that input multi-view image sensor data and infer behavior data that may include, but is not limited to, 3D bounding shapes, instance features, and/or 3D Re-Identification (ReID) feature embeddings that may be used for assigning an object ID and for extending tracking of detected objects within the monitored environment. To generate an ReID feature embedding, an ReID module may aggregate features from different camera views based on visibility scores.

GENERATING SEGMENTATION MASKS FOR OBJECTS IN DIGITAL VIDEOS USING POSE TRACKING DATA

Resumen de: US20260279093A1

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate joint-based segmentation masks for digital objects portrayed in digital videos. In particular, in one or more embodiments, the disclosed systems utilize a video masking model having a pose tracking neural network and a segmentation neural network to generate the joint-based segmentation masks. To illustrate, in some embodiments, the disclosed systems utilize the pose tracking neural network to identify a set of joints of the digital object across the frames of the digital video. The disclosed systems further utilize the segmentation neural network to generate joint-based segmentation masks for the video frames that portray the object using the identified joints. In some cases, the segmentation neural network includes a multi-layer perceptron mixer layer for mixing visual features propagated via convolutional layers.

APPARATUS AND METHOD FOR PROCESSING SCALABLE FEATURE MAPS USING ARTIFICIAL NEURAL NETWORKS

Resumen de: US20260279040A1

A neural processing unit (NPU) for decoding video or feature map is provided. The NPU may comprise at least one processing element (PE) to perform an inference using an artificial neural network. The at least one PE may be configured to receive and decode data included in a bitstream. The data included in the bitstream may comprise data of a base layer. Alternatively, the data included in the bitstream may comprise data of the base layer and data of at least one enhancement layer. The data of the base layer included in the bitstream may include a first feature map. The data of the at least one enhancement layer included in the bitstream may include a second feature map.

PARTIAL DISCHARGE CLASSIFICATION METHOD, DEVICE, AND COMPUTER PROGRAM USING ARTIFICIAL NEURAL NETWORK MODEL

Resumen de: WO2026192201A1

This partial discharge classification method using an artificial neural network model comprises the steps of: generating a prescribed pattern by analyzing time-series data for an acoustic signal; and classifying the type of partial discharge corresponding to the prescribed pattern when the prescribed pattern is input to a trained artificial neural network model.

UNIFIED VISUAL UNDERSTANDING AND IMAGE GENERATION NEURAL NETWORKS

Resumen de: WO2026193311A1

Systems and methods for performing both visual understanding tasks and image generation tasks using the same multi-modal neural network.

Method for Calculating Absorption Rate of Super Absorbent Polymer on Basis of Image Classification Algorithm

Nº publicación: US20260279004A1 17/09/2026

Solicitante:

LG CHEMICAL LTD [KR]
LG Chem, Ltd.

Resumen de: US20260279004A1

A method and system for calculating an absorbing speed of a predetermined absorbent material from video data obtained by photographing a video of the absorbent material absorbing water. Absorption video data is separated into each frame image data, an artificial neural network model is used to determine whether each frame of image data is an absorption-in-progress image or an absorption-completion image, and an absorption start time point and an absorption end time point are detected, thereby calculating the absorption speed of the absorbent material.