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139
resultados
Última actualización
24/10/2026 [08:45:00]
Resumen de: US20260330705A1
0000 A computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene.
Resumen de: US20260332790A1
Apparatuses, systems, and techniques to perform compression of video data using neural networks to facilitate video streaming, such as video conferencing. In at least one embodiment, a sender transmits to a receiver a key frame from video data and one or more keypoints identified by a neural network from said video data, and a receiver reconstructs video data using said key frame and one or more received keypoints.
Resumen de: US20260330202A1
0000 A system and method of mitigating hallucinations may include receiving an input data signal, comprising a sequence of input data segments, and processing the input data segments through an encoder of a transformer-based model, to generate a respective sequence of encoding vectors. The encoding vectors may be processed through a decoder of the transformer-based model, to generate a set of candidate sequences, each representing a first transformation of the input data signal. A deep Neural Network may be applied on the encoding vectors, to obtain a reference sequence, representing a second transformation of the input data signal. Each of the candidate sequences may be aligned with the reference sequence. For each candidate sequence of the set of candidate sequences, a respective probability score may be computed to indicate a quality of its respective alignment. A candidate sequence may be identified as an hallucination, based on its respective probability score.
Resumen de: US20260330826A1
0000 This application relates to the field of sonar technology and discloses a wall recognition method and system for a sonar image, a device, and a storage medium. The method includes: inputting target sonar image data into a pre-trained anomaly detection model for classification prediction of whether each pixel is corresponding to a wall, to obtain a wall recognition result, where training steps for the anomaly detection model include: acquiring training data, using the training data to train an initial model for classification prediction of whether each pixel is corresponding to a wall, to obtain the anomaly detection model, where an anomaly detection network based on a convolutional neural network (CNN) is used for the initial model, and the training data includes sonar image sample data and a target label matrix. accurate presentation of various wall details, and improved wall recognition accuracy.
Resumen de: US20260330820A1
A device for automatically calculating window set information using artificial neural networks according to an embodiment comprises a pre-trained artificial neural network module calculating window set information having a first input information comprising image information as input information, and a window set information extracting window set characteristics from the image information as output information and a feature extractor for extracting a 1-1 feature map and a 1-2 feature map having different scale values for the image information, in the artificial neural network module calculating window set information, wherein the artificial neural network module calculating window set information is generating a 3-1 feature map and a 3-2 feature map by applying convolution to the 1-1 feature map and the 1-2 feature map, respectively, and then determining one of the 3-1 feature map and the 3-2 feature map as a final feature map based on a preset criteria, and then calculating the window set information based on the selected final feature map.
Resumen de: US20260330230A1
A non-transitory computer-readable medium storing instructions that, when executed by one or more computers, cause the one or more computers to: access a room image of a room for room layout estimation; determine, using an encoder-decoder sub-network of a neural network for estimating a layout of a room and the room image, a plurality of predicted two-dimensional (2D) keypoint maps corresponding to a plurality of room types; determine, using the encoder-decoder sub-network, a classifier sub-network of the neural network connected to the encoder-decoder sub-network, and the room image, a predicted room type from the plurality of room types; determine, using the plurality of predicted 2D keypoint maps and the predicted room type, a plurality of ordered keypoints associated with the predicted room type; and determine, using the plurality of ordered keypoints, a predicted layout of the room in the room image.
Resumen de: US20260330245A1
0000 The present application discloses a method and an apparatus for determining a brain-inspired application test suite, an electronic device, and a storage medium. The method includes: performing feature analysis on each brain-inspired application included in an initial application test suite to obtain a data representation feature corresponding to each brain-inspired application; computing a computational overhead generated when each brain-inspired application is executed based on a spiking neural network paradigm; determining a data computation feature corresponding to each brain-inspired application according to the computational overhead; performing clustering processing on the brain-inspired applications in the initial application suite based on the data representation feature and the data computation feature, so that applications having similar features are merged into a same group, obtaining several groups whose main features are pairwise orthogonal; and constructing a representative spiking neural network according to a feature vector corresponding to each group.
Resumen de: US20260330179A1
0000 Embodiments of this specification provide a method and an apparatus for model inference using a cryptographic neural network. The cryptographic neural network includes a plurality of linear layers. The method includes: receiving ciphertext data and key data of a user, where the key data is partial data of an evaluation key of a target format, and the evaluation key of the target format includes a plurality of extended ciphertexts; and inputting the ciphertext data into the cryptographic neural network for cryptographic processing, where the cryptographic processing includes: performing, at any target linear layer in the plurality of linear layers, a ciphertext rotation operation by using a first data part corresponding to a first ciphertext level t in the evaluation key, where the first data part includes only partial data of a single extended ciphertext.
Resumen de: US20260329963A1
A processor-implemented method for reformulating cumulative summation operation in an artificial neural network includes receiving a set of program code. A target operation in the set of program code is identified. The target operation includes a cumulative summation operation. The target operation in the set of program code is reformulated using a first matrix multiplication operation to produce a reformulated target operation. An updated set of program code including the reformulated target operation is output.
Resumen de: US20260330875A1
An image-based search method receives a search input from a user that describes one or more attributes associated with at least one object. The search input is interpreted by applying system attentions and applying a trained neural network that is trained on a training set of syntactical elements and images. A vector is generated based on the interpretation. Images, which may comprise segments of videos, are interpreted by applying system attentions and the trained neural network. The interpretation is applied to generate vectors that correspond to patterns of pixels in the images. The vector associated with the search input is compared to the vectors corresponding to the images and images are selected based on the results of the vector comparisons. Representations of the selected images are provided to the user.
Resumen de: EP4826677A1
0001 Computer-implemented method for upsampling a feature map (F) of an image (x), the method comprising: • Obtaining the image (x) and the feature map (F); • Augmenting the image (x) with respective representations (c) of coordinates of pixels of the image, thereby creating an augmented image (au); • Providing the augmented image (au) as input to a neural network (61), wherein the neural network (61) comprises at least one cross-attention layer (CA) that uses the augmented image (au) or a representation (r) thereof as query input and the feature map (F) as key input; • Providing an output (o) of the neural network (60) as upsampled feature map, wherein the output (o) of the neural network (60) is based on an output of the cross-attention layer (CA).
Resumen de: GB2645395A
Methods, systems, and computer readable media are provided for reduced-dose angiography using machine learning (e.g., deep learning) with a concordance metric indicating a degree of similarity between an estimated segmentation of angiographic images generated by a machine learning model and segmented benchmark angiographic images provided as inputs to the machine learning model. Briefly, techniques described herein may use a neural network trained to conserve/preserve angiographic image quality while reducing the angiographic dose of potentially harmful chemical contrast and/or x-ray radiation by employing a concordance metric.
Resumen de: WO2025128327A1
A method for auditing neural network performance may include receiving a first set of input data for the neural network and training the neural network. The method may further include updating an audit model of the neural network based on the training of the neural network and inputting, to the neural network the first set of input data. A first set of output data associated with the first set of input data may be received from the neural network, and an indication of one or more characteristics of the neural network that impacted the first set of output data may be generated based on the updated audit model of the neural network, the first set of input data, and the first set of output data.
Resumen de: US20260320156A1
0000 Apparatuses, systems, and techniques to train neural networks to perform classification. In at least one embodiment, one or more neural networks are trained to perform classification based, at least in part, on grouping one or more sets of neural network training data according to behaviors of one or more objects within one or more images represented by the training data.
Resumen de: US20260315867A1
0000 An electronic apparatus is disclosed. The electronic apparatus includes: a memory including at least one instruction, a processor coupled with the memory and configured to control the electronic apparatus, and the processor is configured, by executing the at least one instruction, to: obtain a moving image, identify a person and pose data of the person from a plurality of frames in the moving image, obtain exercise pattern information corresponding to the plurality of frames using pose data of the identified person, recognize an exercise motion of the identified person by inputting at least one frame of the moving image into at least one neural network model, and obtain exercise feature information corresponding to the recognized exercise motion, identify, based on the exercise feature information and the exercise pattern information, a first frame interval and a second frame interval different from the first frame interval from among the plurality of frames in the moving image, and provide information on an exercise motion corresponding to the second frame interval by comparing the first frame interval with the second frame interval.
Resumen de: US20260320995A1
Methods and apparatuses are provided for processing of picture data or picture feature data using a neural network with two or more layers. The present disclosure may be applied in the field of artificial intelligence (AI)-based video or picture compression technologies, and in particular, to the field of neural network-based video compression technologies. According to one or more embodiments, two kinds of data are combined during the processing by the neural network. The two kinds of data are obtained from different stages of processing by the network. Some of the advantages may include greater scalability and a more flexible design of the neural network architecture which may further lead to better encoding/decoding performance.
Resumen de: US20260317942A1
To provide a plating apparatus that makes it possible to estimate a current density at a plating-target surface with high precision even if a change in plating conditions occurs. The plating apparatus includes: a sensor configured to measure a potential of a plating solution in a vicinity of a peripheral portion of a substrate held by a substrate holder; and a state estimator configured to calculate, using the measured potential as input, a state estimate representing a current density at the peripheral portion of the substrate, based on an observation model and a state transition model. The state estimator is configured to execute computation based on the observation model by using a first trained neural network model. When a state variable representing the current density at the peripheral portion of the substrate is input, the first trained neural network model is configured to output an estimate representing the potential of the plating solution in the vicinity of the peripheral portion.
Resumen de: US20260319769A1
A compiler of a computing device is described that identifies a sequence of neural network models frequently invoked by an application of the computing device, compiles the models in that sequence, and loads a static random access memory (SRAM) of a hardware accelerator with the compiled models only when the same compiled models-from another, but same, sequence that was previously invoked-are not already present in the SRAM. This prevents unnecessary reloading of compiled models into the SRAM, thereby increasing runtime speed and conserving computational energy.
Resumen de: WO2026214199A1
A braking assistance method and system based on road surface recognition. The braking assistance method comprises: S1, acquiring a road surface image, and processing the road surface image by means of a trained lightweight convolutional neural network, to obtain a road surface type, wherein the lightweight convolutional neural network introduces an inverted residual structure combined with a hybrid attention mechanism and introduces depthwise separable convolution; S2, determining road condition information by combining the road surface type and a preset database; S3, acquiring a rate of change in displacement of a brake pedal, and generating a braking intention of a driver on the basis of the rate of change in displacement of the brake pedal; and S4, dynamically adjusting the distribution of braking force on the basis of the road condition information and the braking intention of the driver, generating a braking control instruction, and sending the braking control instruction to an actuator. By means of the method, when a vehicle travels under adverse road conditions, the vehicle speed can be controlled to maintain vehicle stability, thereby solving the problem of poor dynamic stability in existing vehicle braking assistance.
Resumen de: US20260320267A1
A micromechanics simulation framework has the steps of collecting one or more images of at least one composite material, providing, to a first neural network, an input including the one or more images, segmenting one or more microstructures in the one or more images, quantifying one or more physical descriptors of one or more segmented microstructures, passing an output including the one or more segmented microstructures and one or more quantified physical descriptors to one of: a statistical-based module using descriptor-driven stochastic construction routines with periodic boundary conditions, or a second deep generative neural network, and generating one or more stochastic representative volume element (SRVE) ensembles based on the one or more segmented microstructures and one or more quantified physical descriptors. A micromechanics simulation system is also disclosed for computing the homogenized elastic properties and the time-dependent response of composites in high-temperature environments.
Resumen de: WO2026214606A1
Disclosed in the present application are a lightweight image recognition method and system for a character-wheel camera‑based water meter. The embodiments of the present application implement the collection of a water-meter image, digit recognition and data transmission on the basis of an ESP32 development board, which is connected to a server. In the embodiments of the present application, a neural network is used to perform water‑meter digit recognition, and model parameters are optimized by means of forward propagation and backward propagation algorithms. In the present invention, a development board directly uses a character‑wheel recognition network to perform digit recognition on the current water‑meter image, so as to obtain a water‑meter reading result. It is unnecessary to upload the water‑meter image to a server for reading recognition, thereby helping improve the recognition efficiency of water‑meter readings. Moreover, simply by means of installing the development board on a character-wheel water meter, automatic reading can be implemented, thereby significantly improving the intelligence level of the conventional character-wheel water meter. The system in the embodiments of the present application has the characteristics of a lightweight design and a high efficiency, and is suitable for application scenarios of the automatic reading of smart water meters.
Resumen de: US20260322523A1
An artificial intelligence-based identification method for a wireless composite jamming signal forms a novel neural network structure ACSNet through the integration of an asymmetric convolutional block (ACB), aiming to address composite jamming signal identification in wireless communication systems. The model of the artificial intelligence-based identification method enhances the detection capability of neural networks for subtle signal feature variations based on artificial intelligence, thereby improving the identification accuracy for intricate jamming patterns. The artificial intelligence-based identification method can effectively address the challenge of jamming signal identification under a low jamming-to-noise ratio (JNR). Additionally, ACSNet demonstrates significant resilience to variations in power ratios (PRs) of composite jamming signals, highlighting its adaptability to real-world environments with imbalanced jamming signal power distributions.
Resumen de: WO2026213897A1
Disclosed in the present invention are a face forgery detection method and system based on a graph convolutional neural network. The method comprises: acquiring a facial video to be subjected to forgery detection; and inputting said facial video into a trained detection model, so as to obtain a determination result indicating whether face forgery is present in said video. The detection model comprises: an encoder, which is used for generating features of each image frame on the basis of said facial video; a spatiotemporal-graph construction module, which is used for constructing, on the basis of the features of each image frame, a spatiotemporal graph in which spatial graphs and temporal graphs are integrated; a spatiotemporal-graph convolution module, which is used for performing feature learning on the basis of the spatiotemporal graph, so as to obtain class features; and a classifier, which is used for predicting, on the basis of the class features, a forgery detection result for the facial video. In the present invention, a spatiotemporal graph in which spatial graphs and temporal graphs are integrated is constructed while satisfying the spatiotemporal consistency requirements of a facial video, thereby implementing efficient, accurate and highly-generalizable face forgery detection.
Resumen de: US20260322951A1
0000 Systems, methods and apparatus of vehicle security operations during parking. For example, a vehicle includes: a proximity sensor configured to detect presence of an object approaching the vehicle when the vehicle is in a parking state; at least one camera configured to monitor surroundings of the vehicle when the vehicle is in the parking state; and an artificial neural network configured to extract identification information of the object from images generated by the camera and determine a security classification of the presence of the object. The identification information is stored in the vehicle and/or transmitted to a server or a mobile device, in response to the security classification being in a predefined category.
Nº publicación: US20260321031A1 15/10/2026
Solicitante:
NVIDIA CORP [US]
NVIDIA CORPORATION
Resumen de: US20260321031A1
Panoptic scene completion may be achieved using a neural network comprising a sparse generative encoder-decoder network that performs scene-level occupancy prediction in a fixed-size voxel grid. To segment individual object instances, an instance decoder may segment individual object instances from the scene-level occupancy predictions to predict instance masks in the (occupied) voxel space in a class-agnostic fashion. For each predicted instance (e.g., transformer query), the instance decoder may regress a semantic embedding or token representing the semantic features (e.g., a CLIP token) for a corresponding object instance, enabling zero-shot prompting at test and/or inference time. As such, the neural network may produce semantic features that may be used for open-vocabulary querying directly from 3D measurements, and a representation of the resulting labeled masks may be provided to one or more downstream components such as a control policy of an ego-machine to make decisions on navigation, grasping, and/or manipulation actions.
BOPI
Sede Electrónica
