Patents by Inventor Xingyang NI
Xingyang NI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11831867Abstract: A method comprising: obtaining a configuration of at least one neural network comprising a plurality of intra-prediction mode agnostic layers and one or more intra-prediction mode specific layers, the one or more intra-prediction mode specific layers corresponding to different intra-prediction modes; obtaining at least one input video frame comprising a plurality of blocks; determining to encode one or more blocks using intra prediction; determining an intra-prediction mode for each of said one or more blocks; grouping blocks having same intra-prediction mode into groups, each group being assigned with a computation path among the plurality of intra-prediction mode agnostic and the one or more intra-prediction mode specific layers; training the plurality of intra-prediction mode agnostic and/or the one or more intra-prediction mode specific layers of the neural networks based on a training loss between an output of the neural networks relating to a group of blocks and ground-truth blocks, wherein the ground-tType: GrantFiled: January 29, 2020Date of Patent: November 28, 2023Assignee: Nokia Technologies OyInventors: Francesco Cricri, Caglar Aytekin, Miska Hannuksela, Xingyang Ni
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Patent number: 11657264Abstract: Media content is received for streaming to a user device. A neural network is trained based on a first portion of the media content. Weights of the neural network are updated to overfit the first portion of the media content to provide a first overfitted neural network. The neural network or the first overfitted neural network is trained based on a second portion of the media content. Weights of the neural network or the first overfitted neural network are updated to overfit the second portion of the media content to provide a second overfitted neural network. The first portion and the second portion of the media content are sent with associations to the first overfitted neural network and the second overfitted to the user equipment.Type: GrantFiled: April 9, 2018Date of Patent: May 23, 2023Assignee: Nokia Technologies OyInventors: Francesco Cricri, Caglar Aytekin, Emre Baris Aksu, Miika Sakari Tupala, Xingyang Ni
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Publication number: 20220141455Abstract: A method comprising: obtaining a configuration of at least one neural network comprising a plurality of intra-prediction mode agnostic layers and one or more intra-prediction mode specific layers, the one or more intra-prediction mode specific layers corresponding to different intra-prediction modes; obtaining at least one input video frame comprising a plurality of blocks; determining to encode one or more blocks using intra prediction; determining an intra-prediction mode for each of said one or more blocks; grouping blocks having same intra-prediction mode into groups, each group being assigned with a computation path among the plurality of intra-prediction mode agnostic and the one or more intra-prediction mode specific layers; training the plurality of intra-prediction mode agnostic and/or the one or more intra-prediction mode specific layers of the neural networks based on a training loss between an output of the neural networks relating to a group of blocks and ground-truth blocks, wherein the ground-tType: ApplicationFiled: January 29, 2020Publication date: May 5, 2022Inventors: Francesco CRICRI, Caglar AYTEKIN, Miska HANNUKSELA, Xingyang NI
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Patent number: 11188783Abstract: The invention relates to a method comprising receiving, by a neural network, a first image comprising at least one target object; receiving, by the neural network, a second image comprising at least one query object; and determining, by the neural network, whether the query object corresponds to the target object, wherein the neural network comprises a discriminator neural network of a generative adversarial network (GAN). The invention further relates to an apparatus and a computer program product that perform the method.Type: GrantFiled: October 10, 2018Date of Patent: November 30, 2021Assignee: NOKIA TECHNOLOGIES OYInventors: Francesco Cricrì, Emre Aksu, Xingyang Ni
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Patent number: 11068722Abstract: The invention relates to a method, an apparatus and a computer program product for analyzing media content. The method comprises receiving media content; performing feature extraction of the media content at a plurality of convolution layers to produce a plurality of layer-specific feature maps; transmitting from the plurality of convolution layers a corresponding layer-specific feature map to a corresponding de-convolution layer of a plurality of de-convolution layers via a recurrent connection between the plurality of convolution layers and the plurality of de-convolution layers; and generating a reconstructed media content based on the plurality of feature maps.Type: GrantFiled: September 27, 2017Date of Patent: July 20, 2021Assignee: Nokia Technologies OyInventors: Francesco Cricri, Mikko Honkala, Emre Baris Aksu, Xingyang Ni
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Patent number: 11062210Abstract: A method, apparatus and computer program product provide an automated neural network training mechanism. The method, apparatus and computer program product receive a decoded noisy image and a set of input parameters for a neural network configured to optimize the decoded noisy image. A denoised image is generated based on the decoded noisy image and the set of input parameters. A denoised noisy error is computed representing an error between the denoised image and the decoded noisy image. The neural network is trained using the denoised noisy error and the set of input parameters and a ground truth noisy error value is received representing an error between the original image and the encoded image. The ground truth noisy error value is compared with the denoised noisy error to determine whether a difference between the ground truth noisy error value and the denoised noisy error is within a pre-determined threshold.Type: GrantFiled: October 1, 2019Date of Patent: July 13, 2021Assignee: NOKIA TECHNOLOGIES OYInventors: Caglar Aytekin, Francesco Cricri, Xingyang Ni
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Patent number: 10891524Abstract: The invention relates to a method comprising receiving a set of input samples, said set of input images comprising real images and generated images; extracting a set of feature maps from multiple layers of a pre-trained neural network for both the real images and the generated images; determining statistics for each feature map of the set of feature maps; comparing statistics of the feature maps for the real images to statistics of the feature maps for the generated images by using a distance function to obtain a vector of distances; and averaging the distances of the vector of distances to have a value indicating a diversity of the generated images. The invention also relates to technical equipment for implementing the method.Type: GrantFiled: June 25, 2018Date of Patent: January 12, 2021Assignee: Nokia Technologies OyInventors: Mikko Honkala, Francesco Cricri, Xingyang Ni
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Publication number: 20200104711Abstract: A method, apparatus and computer program product provide an automated neural network training mechanism. The method, apparatus and computer program product receive a decoded noisy image and a set of input parameters for a neural network configured to optimize the decoded noisy image. A denoised image is generated based on the decoded noisy image and the set of input parameters. A denoised noisy error is computed representing an error between the denoised image and the decoded noisy image. The neural network is trained using the denoised noisy error and the set of input parameters and a ground truth noisy error value is received representing an error between the original image and the encoded image. The ground truth noisy error value is compared with the denoised noisy error to determine whether a difference between the ground truth noisy error value and the denoised noisy error is within a pre-determined threshold.Type: ApplicationFiled: October 1, 2019Publication date: April 2, 2020Inventors: Caglar AYTEKIN, Francesco CRICRI, Xingyang NI
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Publication number: 20190311259Abstract: According to the present disclosure, an apparatus includes at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to receive media content for streaming to a user device; to train a neural network to be overfitted to at least a first portion of the media content; and to send the trained neural network and the first portion of the media content to the user equipment. In addition, another apparatus includes at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to receive at least a first portion of media content and a neural network trained to be overfitted to the first portion of the media content; and to process the first portion of the media content using the overfitted neural network.Type: ApplicationFiled: April 9, 2018Publication date: October 10, 2019Inventors: Francesco Cricri, Caglar Aytekin, Emre Baris Aksu, Miika Sakari Tupala, Xingyang Ni
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Publication number: 20190251360Abstract: The invention relates to a method, an apparatus and a computer program product for analyzing media content. The method comprises receiving media content; performing feature extraction of the media content at a plurality of convolution layers to produce a plurality of layer-specific feature maps; transmitting from the plurality of convolution layers a corresponding layer-specific feature map to a corresponding de-convolution layer of a plurality of de-convolution layers via a recurrent connection between the plurality of convolution layers and the plurality of de-convolution layers; and generating a reconstructed media content based on the plurality of feature maps.Type: ApplicationFiled: September 27, 2017Publication date: August 15, 2019Inventors: Francesco Cricri, Mikko Honkala, Emre Baris Aksu, Xingyang Ni
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Publication number: 20190122072Abstract: The invention relates to a method comprising receiving, by a neural network, a first image comprising at least one target object; receiving, by the neural network, a second image comprising at least one query object; and determining, by the neural network, whether the query object corresponds to the target object, wherein the neural network comprises a discriminator neural network of a generative adversarial network (GAN). The invention further relates to an apparatus and a computer program product that perform the method.Type: ApplicationFiled: October 10, 2018Publication date: April 25, 2019Inventors: Francesco Cricrì, Emre Aksu, Xingyang Ni
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Publication number: 20190012581Abstract: The invention relates to a method comprising receiving a set of input samples, said set of input images comprising real images and generated images; extracting a set of feature maps from multiple layers of a pre-trained neural network for both the real images and the generated images; determining statistics for each feature map of the set of feature maps; comparing statistics of the feature maps for the real images to statistics of the feature maps for the generated images by using a distance function to obtain a vector of distances; and averaging the distances of the vector of distances to have a value indicating a diversity of the generated images. The invention also relates to technical equipment for implementing the method.Type: ApplicationFiled: June 25, 2018Publication date: January 10, 2019Inventors: Mikko HONKALA, Francesco CRICRI, Xingyang NI