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公开(公告)号:US20230334321A1
公开(公告)日:2023-10-19
申请号:US17976655
申请日:2022-10-28
Inventor: Su Woong LEE , Jong-Gook KO , Wonyoung YOO , Seungjae LEE , Yongsik LEE , Juwon LEE , Da-Un JUNG
IPC: G06N3/08
Abstract: Disclosed are a deep neural network lightweight device based on batch normalization, and a method thereof. The deep neural network lightweight device based on batch normalization includes a memory that stores at least one data and at least one processor that executes a network lightweight module. When executing the network lightweight module, the processor performs learning on an input neural network based on sparsity regularization to adaptively determine at least one parameter of the sparsity regularization, performs pruning on the learning result, and performs fine tuning on the pruning result.
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公开(公告)号:US20240177473A1
公开(公告)日:2024-05-30
申请号:US18496063
申请日:2023-10-27
Inventor: Jaehwan KIM , Jung Jae YU , Wonyoung YOO , Juwon LEE
CPC classification number: G06V10/82 , G06T7/75 , G06V10/7715 , G06T2207/30196
Abstract: A neural network device for learning dependency of feature data includes: a memory in which at least one program is stored; and a processor that performs a calculation by executing the at least one program, in which the processor is configured to acquire graph information including a data node for a human body; extract feature data corresponding to a plurality of joints constituting the human body from the graph information; acquire a self-attention output corresponding to the feature data based on a self-attention mechanism; and generate result data for a motion of the human body based on the self-attention output, and the self-attention output includes position information acquired based on positional encoding of the feature data and structural information acquired based on geodesic encoding of the feature data.
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公开(公告)号:US20230237629A1
公开(公告)日:2023-07-27
申请号:US18077434
申请日:2022-12-08
Inventor: Juwon LEE , Jung Jae YU , Wonyoung YOO
CPC classification number: G06T5/50 , G06V40/171 , G06T7/11 , G06T3/0068 , G06T3/0093 , G06T2207/30201 , G06T2207/20221
Abstract: A method and apparatus for generating a face-harmonized image are disclosed. The method of generating a face-harmonized image includes receiving an input image, extracting facial landmarks from a target image and the input image, generating a face-removed image of the target image based on a facial mask region, extracting a user face image from the input image, transforming the user face image to correspond to the facial mask region, generating a face-blended image by blending the transformed user face image with the target image, extracting a feature map of the face-blended image, generating a combined feature map based on the feature map of the face-blended image and a feature map of the target image, generating a face harmonization result image based on the combined feature map, and providing the generated face harmonization result image.
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