公开(公告)号：US20220215662A1

公开(公告)日：2022-07-07

申请号：US17557933

申请日：2021-12-21

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Xin YANG ,  Xiaopeng WEI ,  Yu QIAO ,  Qiang ZHANG ,  Baocai YIN ,  Haiyin PIAO ,  Zhenjun DU

IPC: G06V20/40 ,  G06T7/10 ,  G06V10/46 ,  G06V10/82 ,  G06T3/40 ,  G06T7/215 ,  G06T9/00 ,  G06K9/62 ,  G06V10/72 ,  G06V10/764 ,  G06V10/778 ,  G06V10/774 ,  G06V10/776

Abstract: The present invention belongs to the technical field of computer vision, and provides a video semantic segmentation method based on active learning, comprising an image semantic segmentation module, a data selection module based on the active learning and a label propagation module. The image semantic segmentation module is responsible for segmenting image results and extracting high-level features required by the data selection module; the data selection module selects a data subset with rich information at an image level, and selects pixel blocks to be labeled at a pixel level; and the label propagation module realizes migration from image to video tasks and completes the segmentation result of a video quickly to obtain weakly-supervised data. The present invention can rapidly generate weakly-supervised data sets, reduce the cost of manufacture of the data and optimize the performance of a semantic segmentation network.