公开(公告)号：US20220019855A1

公开(公告)日：2022-01-20

申请号：US17488735

申请日：2021-09-29

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hanting CHEN ,  Yunhe WANG ,  Chuanjian LIU ,  Kai HAN ,  Chunjing XU

IPC: G06K9/62 ,  G06N3/04

Abstract: The present application discloses an image generation method, a neural network compression method, and a related apparatus and device in the field of artificial intelligence. The image generation method includes: inputting a first matrix into an initial image generator to obtain a generated image; inputting the generated image into a preset discriminator to obtain a determining result, where the preset discriminator is obtained through training based on a real image and a category corresponding to the real image; updating the initial image generator based on the determining result to obtain a target image generator; and further inputting a second matrix into the target image generator to obtain a sample image. Further, a neural network compression method is disclosed, to compress the preset discriminator based on the sample image obtained by using the foregoing image generation method.

公开(公告)号：US20200167554A1

公开(公告)日：2020-05-28

申请号：US16776282

申请日：2020-01-29

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Liang WANG ,  Songcen XU ,  Chuanjian LIU ,  Jun HE

IPC: G06K9/00 ,  G06F3/01 ,  G06N3/08 ,  G06N20/10 ,  G06K9/62 ,  G06N20/20

Abstract: This application provides a gesture recognition method, and relates to the field of man-machine interaction technologies. The method includes: extracting M images from a first video segment in a video stream; performing gesture recognition on the M images by using a deep learning algorithm, to obtain a gesture recognition result corresponding to the first video segment; and performing result combination on gesture recognition results of N consecutive video segments including the first video segment, to obtain a combined gesture recognition result. In the foregoing recognition process, a gesture in the video stream does not need to be segmented or tracked, but phase actions are recognized by using a deep learning algorithm with a relatively fast calculation speed, and then the phase actions are combined, so as to improve a gesture recognition speed, and reduce a gesture recognition delay.