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TRAINING METHOD AND APPARATUS, VEHICLE SAFETY FUNCTION CONTROL METHOD AND APPARATUS, AND VEHICLE

Resumen de: EP4793797A1

The present disclosure relates to the field of control, and provides a training method and apparatus, a vehicle safety function control method and apparatus, and a vehicle. The training method comprises : acquiring a signal sample image of a vehicle, wherein the signal sample image comprises a safety function normal image and a safety function failure image; using the signal sample image to train a deep neural network model, and using the trained deep neural network model to perform data augmentation processing on the signal sample image to obtain training sample images; and using the training sample images to train a safety function failure identification classifier, wherein the safety function failure identification classifier is used for identifying a safety function state during vehicle operation, and the safety function state includes a safety function normal state or a safety function failure state.

ELECTRONIC APPARATUS AND METHOD FOR CONTROLLING THEREOF

Resumen de: EP4793903A2

A method of controlling an electronic apparatus includes acquiring an image and depth information of the acquired image; inputting the acquired image into a neural network model trained to acquire information on objects included in the acquired image; acquiring an intermediate feature value output by an intermediate layer of the neural network model; identifying a feature area for at least one object among the objects included in the acquired image based on the intermediate feature value; and acquiring distance information between the electronic apparatus and the at least one object based on the feature area for the at least one object and the depth information.

IMAGE PROCESSING DEVICE AND OPERATION METHOD THEREFOR

Resumen de: EP4793878A1

Provided are an image processing device and an operating method of the same. The image processing device includes a memory storing one or more instructions, and at least one processor including processing circuitry, and memory storing one or more instructions that, when executed by the at least one processor individually or collectively, cause the image processing device to obtain a neural network model corresponding to a quality of an input image and viewing information related to the input image. The at least one processor is configured to generate training data, based on the quality of the input image and the viewing information. The at least one processor is configured to train the neural network model by using the training data. The at least one processor is configured to obtain an image quality processed output image from the input image, based on the trained neural network model.

METHOD AND SYSTEM FOR PROFILING PARTICLES IN TAXAS

Resumen de: EP4793908A1

0001 Disclosed is a computer-implemented method for profiling particles in a taxa using a sequence of input data images. The process involves identifying and categorizing suspended particles in each image to obtain bounding boxes and classification data. These particles are then tracked across subsequent images using the bounding boxes to compile tracking data. The method uses this data to output a profile of the suspended particles, incorporating taxonomic identification and possibly using convolutional operations. It employs two neural networks: one for identifying regions of interest and another for categorizing the particles based on these regions. The profile may include biomass calculations and assessments of ecosystem status, integrating sensor metadata such as depth, chlorophyll-a, salinity, and temperature. Non-particle elements like bubbles and damaged areas are excluded from tracking. The method also encompasses a system setup with a camera and processor, and a computer program that enables the execution of these methods.

DATA PROCESSING METHOD AND APPARATUS BASED ON MULTI-MODAL FUSION

Resumen de: EP4793799A1

The present application provides a data processing method and apparatus based on multimodal fusion, pertaining to the technical field of data processing, where the method includes: acquiring one-dimensional data and image data; converting the one-dimensional data into two-dimensional data based on a dimension of the image data; performing zero-padding processing on vacant positions in the two-dimensional data; performing stacking processing on the zero-padded two-dimensional data and the image data to obtain a multilayer stacked input feature map; performing fusion processing on the multilayer stacked input feature map through a neural network to obtain a fused feature map; and performing data processing based on the fused feature map. The present invention can unify the data formats of different modalities, enabling them to be processed in the same feature space, significantly simplifying the alignment process between heterogeneous data.

EXPLOITING INPUT DATA SPARSITY IN NEURAL NETWORK COMPUTE UNITS

Nº publicación: EP4793824A2 19/08/2026

Solicitante:

GOOGLE LLC [US]
Google LLC

Resumen de: EP4793824A2

0001 A hardware circuit for implementing a neural network comprising a plurality of neural network layers comprises a controller. The controller is configured to analyze output activations computed by a first compute system for a first neural network layer, where the output activations are provided on an output activation bus. The controller is further configured to determine which of the output activations have a non-zero value, generate an additional representation of the output activations that identifies only the output activations having a non-zero value, and use the additional representation to supply only the output activations having a non-zero value as input activations to a subsequent, second compute system for a second neural network layer.