公开(公告)号：US20230177646A1

公开(公告)日：2023-06-08

申请号：US18161123

申请日：2023-01-30

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Songjiang LI ,  Takashi ISOBE ,  Xu JIA ,  QI TIAN

IPC: G06T3/40 ,  G06V10/80 ,  G06V10/77 ,  G06V10/74

CPC classification number: G06T3/4053 ,  G06V10/80 ,  G06V10/761 ,  G06V10/7715

Abstract: An image processing method and apparatus in the field of artificial intelligence, including: decomposing a first image to obtain a first structure sub-image and a first detail sub-image, where the first image is any frame of image in video data other than a first frame; fusing first hidden state information and the first structure sub-image to obtain a second structure sub-image, and splicing the first hidden state information and the first detail sub-image to obtain a second detail sub-image; performing feature extraction based on the second structure sub-image and the second detail sub-image to obtain a structure feature and a detail feature; and obtaining an output image based on the structure feature and the detail feature, where resolution of the output image is higher than resolution of the first image.

公开(公告)号：US20230177390A1

公开(公告)日：2023-06-08

申请号：US17892908

申请日：2022-08-22

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Shuo WANG ,  Jun YUE ,  Jianzhuang LIU ,  QI TIAN

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: This application relates to Artificial intelligence and provides a method for training a classifier, one example method including: obtaining a first training sample, where the first training sample includes a corresponding semantic tag; obtaining a plurality of second training samples, where each of the second training samples includes a corresponding semantic tag; determining a target sample from the plurality of second training samples based on semantic similarities between the first training sample and the plurality of second training samples; and training the classifier based on the first training sample, the target sample, and a semantic similarity between the first training sample and the target sample.

公开(公告)号：US20220148328A1

公开(公告)日：2022-05-12

申请号：US17586136

申请日：2022-01-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Qixiang YE ,  Tianliang ZHANG ,  Jianzhuang LIU ,  Xiaopeng ZHANG ,  QI TIAN ,  Lihui JIANG

IPC: G06V40/10 ,  G06V10/25 ,  G06V10/80 ,  G06V10/77 ,  G06V10/44 ,  G06V10/82 ,  G06V20/58 ,  G06V20/52

Abstract: This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: performing feature extraction on an image to obtain a basic feature map of the image; determining a proposal of a region possibly including a pedestrian in the image; processing the basic feature map of the image to obtain an object visibility map in which a response to a pedestrian visible part is greater than a response to a pedestrian blocked part and a background part; performing weighted summation processing on the object visibility map and the basic feature map to obtain an enhanced feature map of the image; and determining, based on the proposal of the image and the enhanced feature map of the image, a bounding box including a pedestrian in the image and a confidence level of the bounding box including the pedestrian in the image.

公开(公告)号：US20220130142A1

公开(公告)日：2022-04-28

申请号：US17573220

申请日：2022-01-11

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yuhui XU ,  Lingxi XIE ,  Xiaopeng ZHANG ,  Xin CHEN ,  Guojun QI ,  QI TIAN

IPC: G06V10/82 ,  G06V10/764 ,  G06N3/10 ,  G06N3/08

Abstract: Example neural architecture search methods and image processing methods and apparatuses in the field of computer vision in the field of artificial intelligence are provided. The example neural architecture search method includes determining search space and a plurality of construction units, superimposing the plurality of construction units to obtain a search network, adjusting, in the search space, network architectures of the construction units in the search network, to obtain optimized construction units, and establishing a target neural network based on the optimized construction units. In each construction unit, some channels of an output feature map of each node are processed by using a to-be-selected operation to obtain a processed feature map, and the processed feature map and a remaining feature map are stitched and then input to a next node.