公开(公告)号：US20240292073A1

公开(公告)日：2024-08-29

申请号：US18656705

申请日：2024-05-07

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Md Ibrahim KHALIL ,  Peng DAI ,  Hanwen LIANG ,  Lizhe CHEN ,  Varshanth Ravindra RAO ,  Juwei LU ,  Songcen XU

IPC: H04N21/8549 ,  G06F3/0484 ,  G06V10/70 ,  G06V20/40

CPC classification number: H04N21/8549 ,  G06V10/70 ,  G06V20/46 ,  G06V20/49 ,  G06F3/0484

Abstract: Methods and devices for generating a customized video segment from a video are disclosed. The video is partitioned into video segments. For each respective video segment, a respective set of scores is computed, where each score represents a respective content feature in the respective video segment. A respective weighted aggregate score is computed for each respective video segment by applying, to each respective set of scores, a common set of weight values. A selected video segment is outputted as the customized video segment, where the selected video segment is selected from one or more high-ranked video segments having high-ranked weighted aggregate scores.

公开(公告)号：US20230072445A1

公开(公告)日：2023-03-09

申请号：US17468224

申请日：2021-09-07

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hanwen LIANG ,  Peng DAI ,  Zhixiang CHI ,  Lizhe CHEN ,  Juwei LU

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: This disclosure provides a training method and apparatus, and relates to the artificial intelligence field. The method includes feeding a primary video segment, representative of a concatenation of a first and a second nonadjacent video segments obtained from a video source, to a deep learning backbone network. The method further includes embedding, via the deep learning backbone network, the primary video segment into a first feature output. The method further includes providing the first feature output to a first perception network to generate a first set of probability distribution outputs indicating a temporal location of a discontinuous point associated with the primary video segment. The method further includes generating a first loss function based on the first set of probability distribution outputs. The method further includes optimizing the deep learning backbone network, by backpropagation of the first loss function.