公开(公告)号：US20250056001A1

公开(公告)日：2025-02-13

申请号：US18931813

申请日：2024-10-30

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Feng LUO ,  Jinxi XIANG ,  Kuan TIAN ,  Jun ZHANG

IPC: H04N19/137 ,  H04N19/172 ,  H04N19/182 ,  H04N19/196 ,  H04N19/42 ,  H04N19/60 ,  H04N19/91

Abstract: This application discloses a video compression method, a video decoding method, and related apparatuses. The method includes: extracting a key point from a to-be-processed video frame and a previous video frame respectively to obtain first position information and second position information; performing motion estimation based on the first position information and the second position information to obtain motion information; performing image inpainting based on the motion information and the previous video frame to obtain an initial video frame; determining a latent feature based on the to-be-processed video frame and the initial video frame; and performing video compression based on the first position information, the second position information, and the latent feature to obtain a video compressed file.

公开(公告)号：US20240249515A1

公开(公告)日：2024-07-25

申请号：US18626165

申请日：2024-04-03

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Zhongyi YANG ,  Sen YANG ,  Jinxi XIANG ,  Jun ZHANG ,  Xiao HAN

IPC: G06V10/82 ,  G06T7/00

CPC classification number: G06V10/82 ,  G06T7/0012 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/032

Abstract: A method for training an image recognition model is performed by a computer device. The method includes: obtaining a sample image and a corresponding sample label; obtaining a sample image patch bag of sample image patches corresponding to the sample image, the sample patch bag having a bag label corresponding to the sample label of the sample image; performing feature analysis on the sample patch bag and the sample image patches in the sample patch bag, respectively, by using an image recognition model; determining a relative entropy loss, a first cross entropy loss corresponding to the sample patch bag and a second cross entropy loss corresponding to the sample image patches based on corresponding bag feature analysis and patch analysis results, respectively; and training the image recognition model based on the relative entropy loss, the first cross entropy loss, and the second cross entropy loss.

公开(公告)号：US20240054760A1

公开(公告)日：2024-02-15

申请号：US18378405

申请日：2023-10-10

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Jinxi XIANG ,  Sen YANG ,  Jun ZHANG ,  Dongxian JIANG ,  Yingyong HOU ,  Xiao HAN

IPC: G06V10/762 ,  G06V10/40 ,  G06V10/764 ,  G06V10/26 ,  G06V10/77 ,  G06V10/80 ,  G06V10/776

CPC classification number: G06V10/762 ,  G06V10/40 ,  G06V10/764 ,  G06V10/267 ,  G06V10/7715 ,  G06V10/806 ,  G06V10/776

Abstract: An image detection method and apparatus are disclosed. The method includes: performing feature extraction processing on the image to obtain a feature representation subset of the image; generating attention weights corresponding to the at least two sub-image features; performing weighting aggregation processing on the at least two sub-image features according to the attention weights to obtain a first feature vector; performing clustering sampling processing on the at least two sub-image features to obtain at least two classification clusters comprising sampled sub-image features; determining a block sparse self-attention for each of the sampled sub-image features according to the at least two classification clusters and a block sparse matrix; determining a second feature vector according to at least two block sparse self-attentions respectively corresponding to the at least two classification clusters; and determining a classification result of the image according to the first feature vector and the second feature vector.

公开(公告)号：US20240273721A1

公开(公告)日：2024-08-15

申请号：US18642802

申请日：2024-04-22

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Sen YANG ,  Jinxi XIANG ,  Jun ZHANG ,  Xiao HAN

IPC: G06T7/00 ,  G06F16/583 ,  G06V10/44 ,  G06V10/74 ,  G06V10/762

CPC classification number: G06T7/0012 ,  G06F16/583 ,  G06V10/44 ,  G06V10/761 ,  G06V10/762 ,  G06T2207/20132 ,  G06T2207/30024

Abstract: A whole slide image (WSI) search method is performed by a computer device, which belong to the field of artificial intelligence. The method includes: acquiring a plurality of tissue images obtained by cropping a WSI; inputting the plurality of tissue images into an image encoder to obtain image feature vectors respectively corresponding to the plurality of tissue images; determining at least one key image from the image feature vectors respectively corresponding to the plurality of tissue images; querying, based on image feature vectors respectively corresponding to the at least one key image, a database for at least one candidate image package respectively corresponding to the at least one key image; and determining at least one screened image package from the at least one candidate image package according to attributes of the at least one candidate image package as search results corresponding to the WSI.

公开(公告)号：US20240273134A1

公开(公告)日：2024-08-15

申请号：US18642807

申请日：2024-04-22

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Sen YANG ,  Jinxi XIANG ,  Jun ZHANG ,  Xiao HAN

IPC: G06F16/535 ,  G06F16/55 ,  G06F16/56 ,  G06V10/26 ,  G06V10/46 ,  G06V10/74 ,  G06V10/762 ,  G06V10/764 ,  G06V10/774

CPC classification number: G06F16/535 ,  G06F16/55 ,  G06F16/56 ,  G06V10/26 ,  G06V10/46 ,  G06V10/761 ,  G06V10/762 ,  G06V10/764 ,  G06V10/774 ,  G06V2201/03

Abstract: Provided is a method for searching for a whole slide image performed by a computer device, which relate to the field of artificial intelligence. The method includes: cropping a whole slide image into a plurality of tissue images; generating, through an image encoder, image feature vectors respectively corresponding to the plurality of tissue images; clustering the image feature vectors respectively corresponding to the plurality of tissue images, to determine at least one key image from the plurality of tissue images; querying, based on image feature vectors respectively corresponding to the at least one key image, a database to obtain at least one target image package corresponding to the at least one key image; and determining a whole slide image to which at least one candidate tissue image comprised in the at least one target image package respectively belongs as a final search result.