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1.发明公开公开(公告)号：US20240320514A1
公开(公告)日：2024-09-26
申请号：US18732399
申请日：2024-06-03
Inventor： Zhenan SUN , Yunlong WANG , Zhengquan LUO , Kunbo ZHANG , Qi LI , Yong HE
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: Disclosed is a method for updating a node model that resists discrimination propagation in federated learning. The method includes: obtaining a node model corresponding to a data node; calculating a mean value of the distribution of class features and a quantity ratio corresponding to training data of the data node, calculating a distribution weighted aggregation model based on the node model, the mean value of the distribution of class features and the quantity ratio; calculating a regularization term corresponding to the data node based on the node model and the distribution weighted aggregation model; calculating a variance of the distribution of the class features corresponding to the data node, calculating a class balanced complementary term by using a cross-domain feature generator; and updating the node model based on the distribution weighted aggregation model, the regularization term, and the class balanced complementary term.
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Patent Agency Ranking
2.发明申请公开(公告)号：US20220262024A1
公开(公告)日：2022-08-18
申请号：US17629373
申请日：2019-10-22
Inventor： Zhenan SUN , Hongwen ZHANG , Wanli OUYANG , Jie CAO
Abstract: A reconstruction method of a three-dimensional (3D) human body model includes: acquiring, by a fully convolutional network (FCN) module, a global UVI map and a local UVI map of a body part according to a human body image (S1); estimating, by a first neural network, a camera parameter and a shape parameter of the human body model based on the global UVI map (S2); extracting, by a second neural network, rotation features of joints of a human body based on the local UVI map (S3); refining, by using a position-aided feature refinement strategy, the rotation features of the joints of the human body to acquire refined rotation features (S4); and estimating, by a third neural network, a pose parameter of the human body model based on the refined rotation features (S5). The reconstruction method achieves accurate and efficient reconstruction of the human body model, and improves robustness of pose estimation.
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公开(公告)号：US20250134430A1
公开(公告)日：2025-05-01
申请号：US19008644
申请日：2025-01-03
Inventor： Zhenan SUN , Yiwei RU , Kunbo ZHANG , Yunlong WANG
Abstract: Disclosed are a method and a system for mental state perception and a computer-readable storage medium. The method includes: acquiring image sequences with timestamps and millimeter-wave radar raw data with timestamps; preprocessing the image sequences and the millimeter-wave radar raw data; analyzing the head region image sequences to obtain head vibration signal features; calculating face region image sequences obtained from preprocessing by using a remote photovolumetric pulse wave recording method to obtain a first heart rate; analyzing the original millimeter-wave radar data sequence to obtain a second heart rate and a breathing rate; fusing the first heart rate, the second heart rate and the breathing rate to obtain a fused heart rate and breathing rate; performing feature extraction on facial change information by a Transformer-like network; establishing a non-contact multi-modal mental perception model for prediction to obtain a predicted result of the mental state.
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公开(公告)号：US20230112462A1
公开(公告)日：2023-04-13
申请号：US17623247
申请日：2021-08-09
Inventor： Qi LI , Zhenan SUN , Yuhao ZHU
IPC: G11B27/031 , G06V20/40 , G06V40/16 , G06V10/26 , G06V20/70 , G06V10/774 , G06V10/80
Abstract: A video generation method includes: obtaining a target face image and a source face image; extracting a feature of each of the source face image and the target face image through a face feature encoder, to obtain corresponding source feature codes and target feature codes; generating swapped face feature codes through a face feature exchanger according to the source feature codes and the target feature codes; generating an initial swapped face image through a face generator according to the swapped face feature codes; and fusing the initial swapped face image with the target face image through a face fuser, to obtain a final swapped face image. The face feature encoder performs hierarchical encoding on the face feature to reserve semantic details of a face, and the face feature exchanger performs further processing based on the hierarchical encoding, to obtain hierarchical encoding of a swapped face feature with semantic details.
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5.发明公开公开(公告)号：US20240320513A1
公开(公告)日：2024-09-26
申请号：US18731260
申请日：2024-06-01
Inventor： Zhenan SUN , Yunlong WANG , Zhengquan LUO , Kunbo ZHANG , Qi LI , Yong HE
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: Disclosed is a disentangled personalized federated learning method via consensus representation extraction and diversity propagation provided by embodiments of the present application. The method includes: receiving, by a current node, local consensus representation extraction models and unique representation extraction models corresponding to other nodes, respectively; extracting, by the current node, the representations of the data of the current node by using the unique representation extraction models of other nodes respectively, and calculating first mutual information between different sets of representation distributions, determining similarity of the data distributions between the nodes based on the size of the first mutual information, and determining aggregation weights corresponding to the other nodes based on the first mutual information; the current node obtains the global consensus representation aggregation model corresponding to the current node.
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公开(公告)号：US20210012093A1
公开(公告)日：2021-01-14
申请号：US17038208
申请日：2020-09-30
Inventor： Qiang RAO , Bing YU , Bailan FENG , Yibo HU , Xiang WU , Ran HE , Zhenan SUN
Abstract: This application provides a method and an apparatus for generating a face rotation image. The method includes: performing pose encoding on an obtained face image based on two or more landmarks in the face image, to obtain pose encoded images; obtaining a plurality of training images each including a face from a training data set, where presented rotation angles of the faces included in the plurality of training images are the same; performing pose encoding on a target face image based on two or more landmarks in the target face image in the foregoing similar manner, to obtain pose encoded images, where the target face image is obtained based on the plurality of training images; generating a to-be-input signal based on the face image and the foregoing two types of pose encoded images; and inputting the to-be-input signal into an face rotation image generative model to obtain a face rotation image.
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