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    METHOD AND APPARATUS WITH IMAGE PROCESSING BASED ON NEURAL DIFFUSION
    1.
    发明申请
    METHOD AND APPARATUS WITH IMAGE PROCESSING BASED ON NEURAL DIFFUSION 有权

    公开(公告)号：US20250095117A1

    公开(公告)日：2025-03-20

    申请号：US18660030

    申请日：2024-05-09

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Seongeun KIM ,  Seohyung LEE

    IPC: G06T5/70 ,  G06T5/60 ,  G06V10/40

    Abstract: A method and apparatus with image processing based on neural diffusion are provided. The method includes: setting a randomness level for a target object; generating a noised image by performing a diffusion process of generating noise images while repeatedly performing noising based on a guide image of a guide domain including the target object and by extracting and saving, based on the randomness level, a partial preservation area from a noise image among the noise images; and obtaining a denoised output image of a target domain by performing a reverse process of repeatedly generating, based on the noised image, denoise images corresponding to the noise images and by applying the saved partial preservation area to a denoise image among the denoise images.

    METHOD AND APPARATUS WITH ADAPTIVE OBJECT TRACKING
    6.
    发明申请
    METHOD AND APPARATUS WITH ADAPTIVE OBJECT TRACKING 有权

    公开(公告)号：US20220138493A1

    公开(公告)日：2022-05-05

    申请号：US17246803

    申请日：2021-05-03

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Seohyung LEE ,  Dongwook LEE ,  Changyong SON ,  SeungWook KIM ,  Byung In YOO

    IPC: G06K9/62 ,  G06K9/32 ,  G06T7/73

    Abstract: Disclosed is a method and apparatus for adaptive tracking of a target object. The method includes method of tracking an object, the method including estimating a dynamic characteristic of an object in an input image based on frames of the input image, determining a size of a crop region for a current frame of the input image based on the dynamic characteristic of the object, generating a cropped image by cropping the current frame based on the size of the crop region, and generating a result of tracking the object for the current frame using the cropped image.

    METHOD AND APPARATUS WITH SELF-ATTENTION-BASED IMAGE RECOGNITION
    7.
    发明公开
    METHOD AND APPARATUS WITH SELF-ATTENTION-BASED IMAGE RECOGNITION 审中-公开

    公开(公告)号：US20230154171A1

    公开(公告)日：2023-05-18

    申请号：US17720681

    申请日：2022-04-14

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Seohyung LEE ,  Dongwook LEE ,  Changbeom PARK ,  Byung In YOO

    IPC: G06V10/82 ,  G06V10/40 ,  G06N3/08

    CPC classification number: G06V10/82 ,  G06N3/08 ,  G06V10/40

    Abstract: A method with self-attention includes: obtaining a three-dimensional (3D) feature map; generating 3D query data and 3D key data by performing a convolution operation based on the 3D feature map; generating two-dimensional (2D) vertical data based on a vertical projection of the 3D query data and the 3D key data; generating 2D horizontal data based on a horizontal projection of the 3D query data and the 3D key data; determining an intermediate attention result through a multiplication based on the 2D vertical data and the 2D horizontal data; and determining a final attention result through a multiplication based on the intermediate attention result and the 3D feature map.

    METHOD AND APPARATUS FOR PROCESSING CONVOLUTION OPERATION ON LAYER IN NEURAL NETWORK
    8.
    发明申请
    METHOD AND APPARATUS FOR PROCESSING CONVOLUTION OPERATION ON LAYER IN NEURAL NETWORK 有权

    公开(公告)号：US20210279568A1

    公开(公告)日：2021-09-09

    申请号：US17015122

    申请日：2020-09-09

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Jaehyoung YOO ,  Jinwoo SON ,  Changyong SON ,  Seohyung LEE ,  Sangil JUNG ,  Changin CHOI

    IPC: G06N3/08 ,  G06N3/04 ,  G06F9/38

    Abstract: Disclosed are methods and apparatuses for processing a convolution operation on a layer in a neural network. The method includes extracting a first target feature vector from a target feature map, extracting a first weight vector matched with the first target feature vector from a first-type weight element, based on matching relationships for depth-wise convolution operations between target feature vectors of the target feature map and weight vectors of the first-type weight element, generating a first intermediate feature vector by performing multiplication between the first target feature vector and the first weight vector, generating a first hidden feature vector by accumulating the first intermediate feature vector and a second intermediate feature vector generated based on a second target feature vector, and generating a first output feature vector of an output feature map based on a point-wise convolution operation between the first hidden feature vector and a second-type weight element.

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